# Publications

## Summary

1/2017

publications: | 13 in journals with IF, 24 in conference proceedings (18 Web of Science/WoS, 29 Scopus/Sco, 32 Google Scholar/GS, 27 RIV) |

citations: | 362 (114 WoS, 162 Sco, 349 GS) − 25 co-citations (14 WoS, 11 Sco, 24 GS) + 39 selfcitations (9 WoS, 24 Sco, 39 GS) |

H-index: | 12 (7 WoS, 8 Sco, 12 GS), without selfcitations 11 (7 WoS, 8 Sco, 11 GS), without self and co-citations 11 (7 WoS, 8 Sco, 11 GS) |

## Citations

## Journal papers

*Journal of Parallel and Distributed Computing***123**(2019), pp. 180–191.

[Elsevier, Parallel exploration of partial solutions in Boolean matrix factorization. DOI 10.1016/j.jpdc.2018.09.014, ISSN 0743-7315]

IF: 1.815, DB: Sco, GSabstract**Abstract**Boolean matrix factorization (BMF) is a well established method for preprocessing and analysis of data. There is a number of algorithms for BMF, but none of them uses benefits of parallelization. This is mainly due to the fact that many of the algorithms utilize greedy heuristics that are inherently sequential. In this work, we propose a general parallelization scheme for BMF in which several locally optimal partial matrix decompositions are constructed simultaneously in parallel, instead of just one in a sequential algorithm. As a result of the computation, either the single best final decomposition or several top-k of them may be returned. The scheme can be applied to any sequential heuristic BMF algorithm and we show the application on two representative algorithms, namely GreConD and Asso. Improvements in decompositions are presented via results from experiments with the new algorithms on synthetic and real datasets.*Information Sciences***459**(2018), pp. 71–85.

[Elsevier, Toward quality assessment of Boolean matrix factorizations. DOI 10.1016/j.ins.2018.05.016, ISSN 0020–0255]

IF: 4.305, DB: WoS (WOS:000441118300005), Sco, GSabstract**Abstract**Boolean matrix factorization has become an important direction in data analysis. In this paper, we examine the question of how to assess the quality of Boolean matrix factorization algorithms. We critically examine the current approaches, and argue that little attention has been paid to this problem so far and that a systematic approach to it is missing. We regard quality assessment of factorization algorithms as a multifaceted problem, identify major views with respect to which quality needs to be assessed, and present various observations on the available algorithms in this regard. Due to its primary importance, we concentrate on the quality of collections of factors computed from data, present a method to assess this quality, and evaluate this method by experiments.*Int. Journal of General Systems***45**(2)(2016), pp. 211–231.

[Taylor & Francis Group, A lattice-free concept lattice update algorithm. DOI 10.1080/03081079.2015.1072928, ISSN 0308–1079 (paper), 1563–5104 (online)]

IF: 1.637, DB: WoS (WOS:000372036100008), Sco, GSabstract | 1 selfcitation (1 Sco, 1 GS)**Abstract**Upon a change of input data, one usually wants an update of output computed from the data rather than recomputing the whole output over again. In Formal Concept Analysis, update of concept lattice of input data when introducing new objects to the data can be done by any of the so-called incremental algorithms for computing concept lattice. The algorithms use and update the lattice while introducing new objects to input data one by one. The present concept lattice of input data without the new objects is thus required by the computation. However, the lattice can be large and may not fit into memory. In this paper, we propose an efficient algorithm for updating the lattice from the present and new objects only, not requiring the possibly large concept lattice of present objects. The algorithm results as a modification of the Close-by-One algorithm for computing the set of all formal concepts, or its modifications like Fast Close-by-One, Parallel Close-by-One or Parallel Fast Close-by-One, to compute new and modified formal concepts and the changes of the lattice order relation only. The algorithm can be used not only for updating the lattice when new objects are introduced but also when some existing objects are removed from the input data or attributes of the objects are changed. We describe the algorithm, discuss efficiency issues and present an experimental evaluation of its performance and a comparison with the AddIntent incremental algorithm for computing concept lattice.**Citations**

In: Huchard M., Kuznetsov S. O. (Eds.):*CLA 2016: Proceedings of the 13th International Conference on Concept Lattices and Their Applications*, 2016, pp. 363–376.

GS Scalable Performance of FCbO Update Algorithm on Museum Data.

*Annals of Mathematics and Artificial Intelligence***72**(1–2)(2014), pp. 3–22.

[Springer, Impact of Boolean factorization as preprocessing methods for classification of Boolean data. DOI 10.1007/s10472-014-9414-x, ISSN 1012–2443 (paper), 1573–7470 (online)]

IF: 0.691, DB: WoS (WOS:000342438500002), Sco, GS, RIVPDF | abstract | 4 citations (2 WoS, 2 Sco, 4 GS) − 1 co-citation (1 GS) + 1 selfcitation (1 Sco, 1 GS)**Abstract**We explore a utilization of Boolean matrix factorization for data preprocessing in classification of Boolean data. In our previous work, we demonstrated that preprocessing that consists in replacing the original Boolean attributes by factors, i.e. new Boolean attributes obtained from the original ones by Boolean matrix factorization, can improve classification quality. The aim of this paper is to explore the question of how the various Boolean factorization methods that were proposed in the literature impact the quality of classification. In particular, we compare five factorization methods, present experimental results, and outline issues for future research.**Citations***Dissertation Thesis*, 2016

GS Decompositions of matrices with relational data: foundations and algorithms.

In:*8th Russian Summer School on Information Retrieval, RuSSIR 2014*,*Communications in Computer and Information Science***505**, 2015, pp. 42–141.

WoS, Sco, GS Introduction to formal concept analysis and its applications in information retrieval and related fields. *Machine Learning***101**(1-3)(2015), pp. 271–302.

WoS, Sco, GS Triadic Formal Concept Analysis and triclustering: searching for optimal patterns.

In:*Proceedings of 2014 IEEE International Conference on Behavior, Economic and Social Computing (BESC 2014)*, 2014, pp. 1–6.

GS Maximum likelihood estimation based DINA model and Q-matrix learning.

In: Yager R., Sgurev V., Hadjiski M., Jotsov V. (Eds.):*Proceedings of the IEEE 8th International Conference on Intelligent Systems, IS 2016*, 2016, pp. 227–233.

WoS, Sco, GS How to assess quality of BMF algorithms?.

*Annals of Mathematics and Artificial Intelligence***70**(1–2)(2014), pp. 151–184.

[Springer, Boolean factors as a means of clustering of interestingness measures of association rules. DOI 10.1007/s10472-013-9370-x, ISSN 1012–2443 (paper), 1573–7470 (online)]

IF: 0.691, DB: WoS (WOS:000334510500008), Sco, GS, RIVPDF | abstract | 4 citations (1 Sco, 4 GS) − 1 co-citation (1 GS)**Abstract**Measures of interestingness play a crucial role in association rule mining. An important methodological problem, on which several papers appeared in the literature, is to provide a reasonable classification of the measures. In this paper, we explore Boolean factor analysis, which uses formal concepts corresponding to classes of measures as factors, for the purpose of clustering of the measures. Unlike the existing studies, our method reveals overlapping clusters of interestingness measures. We argue that the overlap between clusters is a desired feature of natural groupings of measures and that because formal concepts are used as factors in Boolean factor analysis, the resulting clusters have a clear meaning and are easy to interpret. We conduct three case studies on clustering of measures, provide interpretations of the resulting clusters and compare the results to those of the previous approaches reported in the literature.**Citations**

In: Freivalds R. M., Engels G., Catania B. (Eds.):*42nd International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM 2016)*,*Lecture Notes in Computer Science***9587**, 2016, pp. 505–517.

Sco, GS Solving the problem of selecting suitable objective measures by clustering association rules through the measures themselves.

In: Nguyen N. T., Attachoo B., Trawiński B., Somboonviwat K. (Eds.):*Intelligent Information and Database Systems, 6th Asian Conference, ACIIDS 2014, Proceedings, Part I*,*Lecture Notes in Computer Science***8397**, 2014, pp. 221–231.

GS Automated Interestingness Measure Selection for Exhibition Recommender Systems. *Dissertation Thesis*, 2013, 223 pp.

GS Etude comportementale des mesures d'intérêt d'extraction de connaissances. *Applied Artificial Intelligence***26**(3)(2012), pp. 274–301.

GS Fuzzy clustering-based formal concept analysis for association rules mining.

*Fundamenta Informaticae***115**(4)(2012), pp. 395–417.

[IOS Press, Computing formal concepts by attribute sorting. DOI 10.3233/FI-2012-661, ISSN 0169–2968 (paper), 1875–8681 (online)]

IF: 0.399, DB: WoS (WOS:000304190500008), Sco, GS, RIVPDF | abstract | 3 citations (2 WoS, 3 Sco, 3 GS) + 1 selfcitation (1 Sco, 1 GS)**Abstract**We present a novel approach to compute formal concepts of formal context. In terms of operations with Boolean matrices, the presented algorithm computes all maximal rectangles of the input Boolean matrix which are full of 1s. The algorithm combines basic ideas of previous approaches with our recent observations on the influence of attribute permutations and attribute sorting on the number of formal concepts which are computed multiple times. As a result, we present algorithm which computes formal concepts by successive context reduction and attribute sorting. We prove its soundness, discuss its complexity and efficiency, and show that it outperforms other algorithms from the CbO family in terms of substantially lower numbers of formal concepts which are computed multiple times.**Citations***Theoretical Computer Science***658**(2017), pp. 307–315.

Sco, GS Discovery of the D-basis in binary tables based on hypergraph dualization. *Expert Systems with Applications***40**(16)(2013), pp. 6601–6623.

WoS, Sco, GS Formal Concept Analysis in knowledge processing: A survey on models and techniques. *Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery***3**(3)(2013), pp. 200–215.

WoS, Sco, GS Knowledge representation and processing with formal concept analysis.

In: Huchard M., Kuznetsov S. O. (Eds.):*CLA 2016: Proceedings of the 13th International Conference on Concept Lattices and Their Applications*, 2016, pp. 363–376.

GS Scalable Performance of FCbO Update Algorithm on Museum Data.

*Information Sciences***185**(1)(2012), pp. 114–127.

[Elsevier, Fast Algorithm for Computing Fixpoints of Galois Connections Induced by Object-Attribute Relational Data. DOI 10.1016/j.ins.2011.09.023, ISSN 0020–0255]

IF: 3.643, DB: WoS (WOS:000297611600008), Sco, GS, RIVPDF | abstract | 28 citations (13 WoS, 16 Sco, 26 GS) + 5 selfcitations (3 WoS, 5 Sco, 5 GS)**Abstract**Fixpoints of Galois connections induced by object-attribute data tables represent important patterns that can be found in relational data. Such patterns are used in several data mining disciplines including formal concept analysis, frequent itemset and association rule mining, and Boolean factor analysis. In this paper we propose efficient algorithm for listing all fixpoints of Galois connections induced by object-attribute data. The algorithm, called FCbO, results as a modification of Kuznetsov's CbO in which we use more efficient canonicity test. We describe the algorithm, prove its correctness, discuss efficiency issues, and present an experimental evaluation of its performance and comparison with other algorithms.**Citations***International Journal of Machine Learning and Cybernetics*(2016), pp. 1–11.

GS Influence of dynamical changes on concept lattice and implication rules. *Dissertation Thesis*, 2016

GS Decompositions of matrices with relational data: foundations and algorithms. *International Journal of Machine Learning and Cybernetics*(2016), pp. 1–14.

GS Decomposition methods of formal contexts to construct concept lattices. *International Journal of Machine Learning and Cybernetics*(2016), pp. 1–14.

GS Constructing lattice based on irreducible concepts. *International Journal of Conceptual Structures and Smart Applications***4**(1)(2016).

GS Comparision Between Features of CbO based Algorithms for Generating Formal Concepts.

In: Ojeda-Aciego M., Lepskiy A., Ignatov D.I. (Eds.):*2nd International Workshop on Soft Computing Applications and Knowledge Discovery, SCAKD 2016*, 2016, pp. 51–62.

Sco, GS Modification of good tests in dynamic contexts: Application to modeling intellectual development of cadets.

2016.

GS Conceptual Exploration.

In:*Hostile Intent and Counter-Terrorism: Human Factors Theory and Application*,*Human Factors in Defence*, 2015, pp. 161–176.

WoS Tackling Financial and Economic Crime through Strategic Intelligence Management.

In:*Machine Learning and Cybernetics (ICMLC), 2013 International Conference on (Volume:02)*, 2015, pp. 854–859.

WoS The Three-way Object Oriented Concept Lattice and The Three-way Property Oriented Concept Lattice. *International Journal of Applied Mathematics and Computer Science***26**(2)(2016), pp. 495–516.

WoS, Sco, GS A Comprehensive Survey on Formal Concept Analysis, its Research Trends and Applications. *IEEE/ACM Transactions on Computational Biology and Bioinformatics***13**(2)(2016), pp. 380–391.

WoS, Sco, GS Using Formal Concept Analysis to Identify Negative Correlations in Gene Expression Data. *Dissertation Thesis*, 2015, 212 pp.

GS Pathways through online museum collections: designing serendipitous user experiences using formal concept analysis. *Automatic Documentation and Mathematical Linguistics***49**(4)(2015), pp. 109–116.

GS Selection of an algorithm for the parallel implementation of the similarity method in intelligent DSM systems. *Computing and Informatics***34**(1)(2015), pp. 77–98.

WoS, Sco, GS Distributed Computation of Generalized One-sided Concept Lattices on Sparse Data Tables. *Knowledge-based Systems***89**(2015), pp. 411–419.

WoS, Sco, GS A fast incremental algorithm for deleting objects from a concept lattice. *Expert Systems with Applications***42**(9)(2015), pp. 4474–4481.

WoS, Sco, GS A fast incremental algorithm for constructing concept lattices. *Information Sciences***295**(2015), pp. 633–649.

WoS, Sco, GS A ‘Best-of-Breed’ approach for designing a fast algorithm for computing fixpoints of Galois Connections. *The Scientific World Journal***2014**(2014).

WoS, Sco, GS Deep First Formal Concept Search.

In: Hernandez N., Jäschke R., Croitoru M. (Eds.):*Graph-Based Representation and Reasoning, 21st International Conference on Conceptual Structures, ICCS 2014. Proceedings*,*Lecture Notes in Artificial Intelligence***8577**, 2014, pp. 37–50.

WoS, Sco, GS A Partial-Closure Canonicity Test to Increase the Efficiency of CbO-Type Algorithms.

In: Larsen H. L., Martin-Bautista M. J., Vila M. A., Andreasen T., Christiansen H. (Eds.):*Flexible Query Answering Systems, 10th International Conference, FQAS 2013, Proceedings*,*Lecture Notes in Computer Science***8132**, 2013, pp. 124–133.

Sco, GS Using Formal Concept Analysis to Detect and Monitor Organised Crime.

In: Cellier P., Distel F., Ganter B. (Eds.):*Formal Concept Analysis, 11th International Conference, ICFCA 2013. Proceedings*,*Lecture Notes in Computer Science***7880**, 2013, pp. 76–91.

Sco, GS Using Pattern Structures for Analyzing Ontology-Based Annotations of Biomedical Data. *Dissertation Thesis*, 2013, 91 pp.

GS Redes Sociais e Classificação Conceptual: Abordagem Complementar para um sistema de Recomendação de Coautorias. *Information Sciences***222**(2013), pp. 611–625.

WoS, Sco, GS Rough set model based on formal concept analysis. *Abstract and Applied Analysis*(2013).

WoS, Sco, GS Attribute Reduction in Intuitionistic Fuzzy Concept Lattices. *Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery***3**(3)(2013), pp. 200–215.

WoS, Sco, GS Knowledge representation and processing with formal concept analysis.

In: Andrews S., Dau F. (Eds.):*The 2nd CUBIST Workshop (CUBIST-WS-12), Proceedings*, 2012, pp. 33–52.

GS ConSeQueL - SQL Preprocessor Using Formal Concept Analysis with Measures.

In:*Machine Learning and Cybernetics (ICMLC), 2012 International Conference on (Volume:4)*, 2012, pp. 1326–1332.

Sco, GS Computing the set of concepts through the composition and decomposition of formal contexts.

In: Szathmary L., Priss U. (Eds.):*Proc. CLA 2012*, 2012, pp. 9–20.

Sco, GS A Generalized Next-Closure Algorithm -- Enumerating Semilattice Elements from a Generating Set.

In: Huchard M., Kuznetsov S. O. (Eds.):*CLA 2016: Proceedings of the 13th International Conference on Concept Lattices and Their Applications*, 2016, pp. 363–376.

GS Scalable Performance of FCbO Update Algorithm on Museum Data. *Int. Journal of General Systems***45**(2)(2016), pp. 211–231.

WoS, Sco, GS A lattice-free concept lattice update algorithm. *Annals of Mathematics and Artificial Intelligence***72**(1–2)(2014), pp. 3–22.

WoS, Sco, GS Impact of Boolean factorization as preprocessing methods for classification of Boolean data.

In: Ojeda-Aciego M., Outrata J. (Eds.):*CLA 2013: Proceedings of the 10th International Conference on Concept Lattices and Their Applications*, 2013, pp. 261–274.

Sco, GS A lattice-free concept lattice update algorithm based on *CbO. *Fundamenta Informaticae***115**(4)(2012), pp. 395–417.

WoS, Sco, GS Computing formal concepts by attribute sorting.

*Annals of Mathematics and Artificial Intelligence***59**(2)(2010), pp. 257–272.

[Springer, Parallel Algorithm for Computing Fixpoints of Galois Connections. DOI 10.1007/s10472-010-9199-5, ISSN 1012–2443 (paper), 1573–7470 (online)]

IF: 0.430, DB: WoS (WOS:000286599400008), Sco, GS, RIVPDF | abstract | 26 citations (10 WoS, 15 Sco, 24 GS) − 1 co-citation (1 GS) + 5 selfcitations (3 WoS, 5 Sco, 5 GS)**Abstract**This paper presents a parallel algorithm for computing fixpoints of Galois connections induced by object-attribute relational data. The algorithm results as a parallelization of CbO in which we process disjoint sets of fixpoints simultaneously. One of the distinctive features of the algorithm compared to other parallel algorithms is that it avoids synchronization which has positive impacts on its speed and implementation. We describe the parallel algorithm, prove its correctness, and analyze its asymptotic complexity. Furthermore, we focus on implementation issues, scalability of the algorithm, and provide an evaluation of its efficiency on various data sets.**Citations**

In: Hassanien A. E., Shaalan K., Azar A. T., Gaber T.,Tolba M. F. (Eds.):*2nd International Conference on Advanced Intelligent Systems and Informatics, AISI 2016*, 2017, pp. 781–792.

Sco, GS Enhanced algorithms for fuzzy formal concepts analysis. *International Journal of Machine Learning and Cybernetics*(2016), pp. 1–14.

GS Decomposition methods of formal contexts to construct concept lattices. *International Journal of Machine Learning and Cybernetics*(2016), pp. 1–14.

GS Constructing lattice based on irreducible concepts. *International Journal of Machine Learning and Cybernetics*(2016), pp. 1–9.

GS Multi-scaled concept lattices based on neighborhood systems. *Artificial Intelligence Research***5**(2)(2016).

GS Parallelization of the next Closure algorithm for generating the minimum set of implication rules. *Information Sciences***349**(2016), pp. 199–215.

WoS, Sco, GS Genetic generation of fuzzy systems with rule extraction using formal concept analysis. *International Journal of Machine Learning and Cybernetics***7**(4)(2016), pp. 539–552.

WoS, Sco, GS Concept lattice compression in incomplete contexts based on K-medoids clustering. *Dissertation Thesis*, 2015, 212 pp.

GS Pathways through online museum collections: designing serendipitous user experiences using formal concept analysis.

In:*8th IEEE International Workshop on Computational Intelligence and Applications (IWCIA 2015)*, 2015, pp. 103–108.

WoS, Sco, GS Towards parallel mining of closed patterns from multi-relational data.

In: IEEE (Ed.):*2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)*,*IEEE International Conference on Fuzzy Systems*, 2015.

WoS, Sco, GS FCA-BASED RULE GENERATOR, a framework for the genetic generation of fuzzy classification systems using formal concept analysis. *Journal of Advanced Computational Intelligence and Intelligent Informatics***19**(6)(2015), pp. 804–809.

WoS, Sco Distributed Mining of Closed Patterns from Multi-Relational Data. *Computing and Informatics***34**(1)(2015), pp. 77–98.

WoS, Sco, GS Distributed Computation of Generalized One-sided Concept Lattices on Sparse Data Tables.

In:*2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS 2014) and 15th International Symposium on Advanced Intelligent Systems (ISIS 2014)*, 2014, pp. 1138–1141.

WoS, Sco, GS Towards efficient closed pattern mining from distributed multi-relational data.

In: Bertet K., Rudolph S. (Eds.):*Proc. CLA 2014*, 2014, pp. 71–83.

Sco, GS Merging Closed Pattern Sets in Distributed Multi-Relational Data. *Soft Computing***18**(4)(2014), pp. 683–694.

WoS, Sco, GS A new FCA algorithm enabling analyzing of complex and dynamic data sets.

2013

GS Анализ тональности текстов с применением ДСМ-метода. *International Journal of Intelligent Systems***28**(1)(2013), pp. 93–114.

WoS, Sco, GS Finding Fuzzy Concepts for Creative Knowledge Discovery.

In: Szathmary L., Priss U. (Eds.):*Proc. CLA 2012*, 2012, pp. 115–126.

Sco Distributed closed pattern mining in multi-relational data based on iceberg query lattices: Some preliminary results.

In:*Machine Learning and Cybernetics (ICMLC), 2012 International Conference on (Volume:4)*, 2012, pp. 1326–1332.

Sco, GS Computing the set of concepts through the composition and decomposition of formal contexts. *Dissertation Thesis*, 2012, 77 pp.

GS Concept analysis of three-way ordinal matrices.

In: IEEE (Ed.):*2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)*,*IEEE International Conference on Fuzzy Systems*, 2012.

WoS, Sco, GS Using Fuzzy Formal Concepts in the Genetic Generation of Fuzzy Systems. *Technical Report*, 2011, 28 pp.

GS An Approach for the Extraction of Classification Rules from Fuzzy Formal Contexts.

In: Prati C. R., Dimuro G. P., Cunha Campos A. M. (Eds.):*VIII Encontro Nacional de Inteligência Artificial (ENIA 2011), XXXI Congresso da Sociedade Brasileira de Computação (CSBC 2011), Proceedings*, 2011.

GS On Rule Generation Approaches for Genetic Fuzzy Systems.

In:*Proceedings of the 11th UK Workshop on Computational Intelligence*, 2011, pp. 61–67.

GS Fuzzy formal concept analysis and algorithm.

In: Napoli A., Vychodil V. (Eds.):*Proc. CLA 2011*, 2011, pp. 413–416.

Sco, GS Formal Concept Analysis on Graphics Hardware.

In: Adams N. M., Robardet C., Siebes A., Boulicaut J.-F. (Eds.):*Advances in Intelligent Data Analysis VIII, 8th International Symposium on Intelligent Data Analysis, IDA 2009*,*Lecture Notes in Computer Science***5772**, 2009, pp. 333–344.

GS Distributed Algorithm for Computing Formal Concepts Using Map-Reduce Framework.

In: Huchard M., Kuznetsov S. O. (Eds.):

GS Scalable Performance of FCbO Update Algorithm on Museum Data. *Int. Journal of General Systems***45**(2)(2016), pp. 211–231.

WoS, Sco, GS A lattice-free concept lattice update algorithm.

In: Ojeda-Aciego M., Outrata J. (Eds.):*CLA 2013: Proceedings of the 10th International Conference on Concept Lattices and Their Applications*, 2013, pp. 261–274.

Sco, GS A lattice-free concept lattice update algorithm based on *CbO. *Fundamenta Informaticae***115**(4)(2012), pp. 395–417.

WoS, Sco, GS Computing formal concepts by attribute sorting. *Information Sciences***185**(1)(2012), pp. 114–127.

WoS, Sco, GS Fast Algorithm for Computing Fixpoints of Galois Connections Induced by Object-Attribute Relational Data.

*IEEE Transactions on Fuzzy Systems***18**(3)(2010), pp. 546–557.

[IEEE, Computing the lattice of all fixpoints of a fuzzy closure operator. DOI 10.1109/TFUZZ.2010.2041006, ISSN 1063–6706]

IF: 2.695, DB: WoS (WOS:000278538000009), Sco, GS, RIVPDF | abstract | 47 citations (25 WoS, 36 Sco, 44 GS) − 4 co-citations (4 WoS, 3 Sco, 4 GS)**Abstract**We present a fast bottom-up algorithm for computing all fixpoints of a fuzzy closure operator in a finite set over a finite chain of truth degrees, along with the partial order on the set of all fixpoints. Fuzzy closure operators appear in several areas of fuzzy logic and its applications, including formal concept analysis which we use as a reference area of application in this paper. Several problems in formal concept analysis, such as computing all formal concepts from data with graded attributes or computing non-redundant bases of all attribute dependencies, can be reduced to the problem of computing fixpoints of particular fuzzy closure operators associated with the input data. The development of a general algorithm applicable in particular to these problems is the ultimate purpose of this paper. We present the algorithm, its theoretical foundations, and experimental evaluation.**Citations**

In: Hassanien A. E., Shaalan K., Azar A. T., Gaber T.,Tolba M. F. (Eds.):*2nd International Conference on Advanced Intelligent Systems and Informatics, AISI 2016*, 2017, pp. 781–792.

Sco, GS Enhanced algorithms for fuzzy formal concepts analysis.

In:*2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016*, 2016, pp. 209–216.

Sco, GS Unifying fuzzy concept lattice construction methods. *International Journal of Machine Learning and Cybernetics*(2016), pp. 1–11.

GS A data reduction method in formal fuzzy contexts. *Intelligent Automation & Soft Computing*(2016), pp. 1–15.

GS An intensive study on rule acquisition in formal decision contexts based on minimal closed label concept lattices.

In: N. L. Kazanskiy, D. V. Kudryashov, S. B. Popov, V. V. Sergeev, R. V. Skidanov, V. A. Fursov, V. A. Sobolev (Eds.):*Proc. Information Technology and Nanotechnology (ITNT 2016)*, 2016, pp. 796–805.

GS Intelligent analysis of incomplete data for building formal ontologies. *IEEE Transactions On Fuzzy Systems***24**(3)(2016), pp. 645–660.

WoS, Sco, GS Parameterizing the Semantics of Fuzzy Attribute Implications by Systems of Isotone Galois Connections. *Computer Science***42**(8)(2015), pp. 288–318.

GS Construction Algorithm of Fuzzy Concept Lattice Based on Constraints. *International Journal of Machine Learning and Cybernetics*(2015), pp. 1–14.

GS Concept granular computing systems and their approximation operators. *ResearchGate*, 2015

GS Ranks of fuzzy matrices. Applications in state reduction of fuzzy automata. *Annals of Fuzzy Mathematics and Informatics*(2015).

GS Rules for Computing Fixpoints of a Fuzzy Closure Operator. *Knowledge-Based Systems***73**(2015), pp. 265–275.

WoS, Sco, GS Knowledge reduction in formal fuzzy contexts. *International Journal of Computer Mathematics***92**(9)(2015), pp. 1855–1873.

WoS, Sco, GS On the use of thresholds in multi-adjoint concept lattices. *International Journal of Advanced Intelligence Paradigms***6**(4)(2014), pp. 272–311.

Sco, GS Fuzzy information retrieval in WWW: a survey. *Kongzhi yu Juece/Control and Decision***29**(11)(2014), pp. 1935–1942.

Sco, GS Load balance-based algorithm for parallelly generating fuzzy formal concepts.

In: Gibbs M. (Ed.):*Norbert Wiener in the 21st Century (21CW), 2014 IEEE Conference on*, 2014, pp. 1–8.

WoS, Sco, GS Fuzzy ontologies: The state of the art. *Knowledge-Based Systems***65**(2014), pp. 1–11.

WoS, Sco, GS Relations between granular reduct and dominance reduct in formal contexts. *Information Sciences***266**(2014), pp. 218–225.

WoS, Sco, GS Using concept lattice theory to obtain the set of solutions of multi-adjoint relation equations. *Applied Intelligence***40**(1)(2014), pp. 154–177.

WoS, Sco, GS Formal and relational concept analysis for fuzzy-based automatic semantic annotation. *International Journal of General Systems***43**(2)(2014), pp. 105–134.

WoS, Sco, GS Fuzzy and rough formal concept analysis: a survey. *Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence***26**(3)(2013), pp. 260–269.

Sco A parallel algorithm generating fuzzy formal concepts.

In: Cellier P., Distel F., Ganter B. (Eds.):*Contributions to the 11 th International Conference on Formal Concept Analysis (ICFCA 2013)*, 2013, pp. 5–18.

GS Heterogeneous environment on examples.

In:*Machine Learning and Cybernetics (ICMLC), 2013 International Conference on (Volume:01)*, 2013, pp. 124–129.

WoS, Sco, GS Attribute reduction and attribute characteristics of formal contexts.

In: Pasi G., Montero J., Ciucci D. (Eds.):*Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)*,*Advances in Intelligent Systems Research***32**, 2013, pp. 302–309.

WoS, Sco, GS On Different Types of Heterogeneous Formal Contexts. *Fundamenta Informaticae***126**(4)(2013), pp. 397–414.

WoS, Sco, GS Vector-based Attribute Reduction Method for Formal Contexts. *International Journal of Intelligent Systems***28**(1)(2013), pp. 93–114.

WoS, Sco, GS Finding Fuzzy Concepts for Creative Knowledge Discovery. *Expert Systems with Applications***40**(16)(2013), pp. 6601–6623.

WoS, Sco, GS Formal Concept Analysis in knowledge processing: A survey on models and techniques. *Information Sciences***253**(2013), pp. 100–109.

WoS, Sco, GS Multi-adjoint relation equations: Definition, properties and solutions using concept lattices. *Information Sciences***225**(2013), pp. 47–54.

WoS, Sco, GS Dual multi-adjoint concept lattices. *Information Sciences***222**(2013), pp. 405–412.

WoS, Sco, GS Solving systems of fuzzy relation equations by fuzzy property-oriented concepts.

In: Pasi G., Montero J., Ciucci D. (Eds.):*Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)*,*Advances in Intelligent Systems Research***32**, 2013, pp. 340–346.

WoS, Sco, GS Building multi-adjoint concept lattices.

In:*12th International Multidisciplinary Scientific GeoConference and EXPO - Modern Management of Mine Producing, Geology and Environmental Protection, SGEM 2012 (Volume 5), Proceedings*, 2012, pp. 13–20.

Sco, GS A Slovak environmental rating tool for buildings.

In: Greco S., Bouchon-Meunier B., Coletti G., Fedrizzi M., Matarazzo B., Yager R. R. (Eds.):*Advances in Computational Intelligence, 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Proceedings, Part II*,*Communications in Computer and Information Science***298**, 2012, pp. 395–404.

Sco, GS Solving General Fuzzy Relation Equations Using Property-Oriented Concept Lattices.

In: Greco S., Bouchon-Meunier B., Coletti G., Fedrizzi M., Matarazzo B., Yager R. R. (Eds.):*Advances in Computational Intelligence, 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Proceedings, Part III*,*Communications in Computer and Information Science***299**, 2012, pp. 221–230.

Sco, GS Algorithms for Computation of Concept Trilattice of Triadic Fuzzy Context.

In: Szathmary L., Priss U. (Eds.):*Proc. CLA 2012*, 2012, pp. 9–20.

Sco, GS A Generalized Next-Closure Algorithm -- Enumerating Semilattice Elements from a Generating Set. *Dissertation Thesis*, 2012, 77 pp.

GS Concept analysis of three-way ordinal matrices. *Journal of Logic and Computation***22**(6)(2012), pp. 1405–1425.

WoS, Sco, GS Optimal decompositions of matrices with entries from residuated lattices. *Fuzzy Sets and Systems***208**(2012), pp. 95–110.

WoS, Sco, GS On multi-adjoint concept lattices based on heterogeneous conjunctors.

In:*2011 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS'2011*, 2011.

Sco Comparing two approaches to creating fuzzy concept lattices.

In: Napoli A., Vychodil V. (Eds.):*Proc. CLA 2011*, 2011, pp. 75–86.

Sco, GS Concept lattices in fuzzy relation equations.

In:*Proceedings of the 11th UK Workshop on Computational Intelligence*, 2011, pp. 61–67.

GS Fuzzy formal concept analysis and algorithm. *Fuzzy Optimization and Decision Making***10**(4)(2011), pp. 287–309.

WoS, Sco, GS Possibility-theoretic extension of derivation operators in formal concept analysis over fuzzy lattices.

In: IEEE (Ed.):*IEEE International Conference on Fuzzy Systems (FUZZ 2011)*,*IEEE International Conference on Fuzzy Systems*, 2011, pp. 1743–1750.

WoS, Sco, GS Fuzzy Concept Lattice Construction A Basis for Building Fuzzy Ontologies.

In: Yao J. T., Ramanna S., Wang G. Y., Suraj Z. (Eds.):*Rough Sets and Knowledge Technology*,*Lecture Notes in Artificial Intelligence***6954**, 2011, pp. 71–80.

WoS, Sco, GS A Novel Attribute Reduction Approach Based on the Object Oriented Concept Lattice.

In: Kuznetsov S. O., Slezak D., Hepting D. H., Mirkin B. G. (Eds.):*Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGRC 2011*,*Lecture Notes in Artificial Intelligence***6743**, 2011, pp. 127–134.

WoS, Sco, GS Creating Fuzzy Concepts: The One-Sided Threshold, Fuzzy Closure and Factor Analysis Methods.

In: Belohlavek R., Klir G. J. (Eds.):*Concepts and Fuzzy Logic*, 2011, pp. 177–207.

WoS, GS Formal Concept Analysis: Classical and Fuzzy.

In: Kuznetsov S. O., Slezak D., Hepting D. H., Mirkin B. G. (Eds.):*Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGRC 2011*,*Lecture Notes in Artificial Intelligence***6743**, 2011, pp. 19–26.

WoS, Sco, GS What is a Fuzzy Concept Lattice? II.

In: Wierman M. (Ed.):*2010 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), Proceedings*, 2010, pp. 1–6.

Sco Fuzzy concept lattices: Examples using the Gene Ontology.

*Int. Journal of General Systems***38**(4)(2009), pp. 455–467.

[Taylor & Francis Group, Inducing decision trees via concept lattices. DOI 10.1080/03081070902857563, ISSN 0308–1079 (paper), 1563–5104 (online)]

IF: 0.611, DB: WoS (WOS:000265295900006), Sco, GS, RIVPDF | abstract | 26 citations (13 WoS, 20 Sco, 26 GS) − 1 co-citation (1 WoS, 1 Sco, 1 GS)**Abstract**We present a novel method for the construction of decision trees. The method utilises concept lattices in that certain formal concepts of the concept lattice associated to input data are used as nodes of the decision tree constructed from the data. The concept lattice provides global information about natural clusters in the input data, which we use for selection of splitting attributes. The usage of such global information is the main novelty of our approach. Experimental evaluation indicates good performance of ourmethod. We describe the method, experimental results, and a comparison with standard methods on benchmark datasets.**Citations***Computing*(2017), pp. 1–26.

GS A set of measures designed to identify overlapped instances in software defect prediction.

In: Ignatov D. I., Khachay M. Y., Labunets V. G., Loukachevitch N., Nikolenko S., Panchenko A., Savchencko A. V., Vorontosov K. V. (Eds.):*Supplementary Proceedings of the Fifth International Conference on Analysis of Images, Social Networks and Texts (AIST 2016)*, 2016, pp. 73–84.

GS Lazy Learning of Classification Rules for Complex Structure Data.

In: Huchard M., Kuznetsov S. O. (Eds.):*Proc. CLA 2016*, 2016, pp. 189–201.

Sco, GS Global Optimization in Learning with Important Data: an FCA-Based Approach.

In: Andrews S., Polovina S. (Eds.):*Proc. Fifth Conceptual Structures Tools & Interoperability Workshop (CSTIW 2016), 22nd International Conference on Conceptual Structures (ICCS 2016)*, 2016, pp. 1–9.

Sco, GS A Tool for Creating and Visualising Formal Concept Trees.

In:*8th Russian Summer School on Information Retrieval, RuSSIR 2014*,*Communications in Computer and Information Science***505**, 2015, pp. 42–141.

WoS, Sco, GS Introduction to formal concept analysis and its applications in information retrieval and related fields.

In: Broome B. D., Hanratty T. P., Hall D. L., Llinas J. (Eds.):*Proc. SPIE 9499, Next-Generation Analyst III, 949908*, 2015.

WoS, Sco, GS Classification of short-lived objects using an interactive adaptable assistance system. *Machine Learning***101**(1-3)(2015), pp. 271–302.

WoS, Sco, GS Triadic Formal Concept Analysis and triclustering: searching for optimal patterns. *International Journal of Computational Intelligence Systems***8**(1)(2015), pp. 175–186.

WoS, Sco, GS On inference rules in decision formal contexts. *Knowledge-Based Systems***75**(2015), pp. 78–86.

WoS, Sco, GS Approximate concepts acquisition based on formal contexts. *International Journal of Computational Intelligence Systems***7**(6)(2014), pp. 1044–1053.

Sco, GS Attribute reduction based on maximal rules in decision formal context. *The Scientific World Journal*(2014).

WoS, Sco, GS Improving Predictions of Multiple Binary Models in ILP. *Dissertation Thesis*, 2013

GS Vers une approche hybride mêlant arbre de classification et treillis de Galois pour de l'indexation d'images.

In: Carpineto C., Kuznetsov S. O., Napoli A. (Eds.):*FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013)*, 2013, pp. 22–35.

Sco, GS Classification by Selecting Plausible Formal Concepts in a Concept Lattice. *Expert Systems with Applications***40**(16)(2013), pp. 6601–6623.

WoS, Sco, GS Formal Concept Analysis in knowledge processing: A survey on models and techniques. *Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery***3**(3)(2013), pp. 200–215.

WoS, Sco, GS Knowledge representation and processing with formal concept analysis. *International Journal of Approximate Reasoning***54**(1)(2013), pp. 149–165.

WoS, Sco, GS Incomplete decision contexts: Approximate concept construction, rule acquisition and knowledge reduction.

In: Игнатов Д.И., Яворский Р.Э. (Eds.):*Анализ Изображений Сетей и Текстов*, 2012, pp. 1–12.

GS Анализ формальных понятий: от теории к практике. *International Journal of General Systems***41**(2)(2012), pp. 143–161.

WoS, Sco, GS Knowledge reduction in formal decision contexts based on an order-preserving mapping.

In: Yao JT., Ramanna S., Wang G., Suraj Z. (Eds.):*Rough Set and Knowledge Technology, 6th International Conference, RSKT 2011*,*Lecture Notes in Computer Science***6954**, 2011, pp. 51–56.

Sco, GS Dependence and Algebraic Structure of Formal Contexts. *Raf. J. of Comp. & Math's.***8**(1)(2011), pp. 79–87.

GS Intrusion Detection System Based on Decision Tree and Clustered Continuous Inputs. *Journal of Information and Computational Science***8**(16)(2011), pp. 4201–4208.

Sco, GS Algorithm for Construction of Evolution-Based Concept Lattices with Application to Public Sentiment Prediction. *Knowledge-Based Systems***24**(5)(2011), pp. 709–715.

WoS, Sco, GS Knowledge reduction in decision formal contexts. *Knowledge-Based Systems***23**(6)(2010), pp. 504–511.

Sco, GS Approaches to attribute reduction in concept lattices induced by axialities. *International Journal of General Systems***39**(3)(2010), pp. 255–270.

WoS, Sco, GS Interval-valued linguistic variables: an application to the L-fuzzy contexts with absent values. *Traitement du Signal***26**(5)(2009), pp. 409–418.

GS Treillis dichotomiques et arbres de décision.

In: Torra V., Narukawa Y., Inuiguchi M. (Eds.):*Modeling Decisions for Artificial Intelligence, Proceedings*,*Lecture Notes in Artificial Intelligence***5861**, 2009, pp. 114–125.

WoS, Sco, GS Comparison of Data Structures for Computing Formal Concepts.

*Int. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems***16**(1)(2008), pp. 1–15.

[World Scientific, Characterizing trees in concept lattices. DOI 10.1142/S0218488508005212, ISSN 0218–4885 (paper), 1793–6411 (online)]

IF: 1.000, DB: WoS (WOS:000256307600002), Sco, GS, RIVPDF | abstract | 6 citations (3 WoS, 1 Sco, 5 GS)**Abstract**Concept lattices are systems of conceptual clusters, called formal concepts,which are partially ordered by the subconcept/superconcept relationship. Concept lattices are basic structures used in formal concept analysis. In general, a concept lattice may contain overlapping clusters and need not be a tree. On the other hand, tree-like classification schemes are appealing and are produced by several clustering methods. In this paper, we present necessary and sufficient conditions on input data for the output conceptlattice to form a tree after one removes its least element. We present these conditions for input data with yes/no attributes as well as for input data with fuzzy attributes. In addition, we show how Lindig's algorithm for computing concept lattices gets simplified when applied to input data for which the associated concept lattice is a tree after removing the least element. The paper also contains illustrative examples.**Citations***Soft Computing*(2016), pp. 1–19.

GS Representing attribute reduction and concepts in concept lattice using graphs. *Armenian Journal of Mathematics***8**(2)(2016), pp. 86–95.

WoS, GS Characterizing trees in property-oriented concept lattices. *International Journal of General Systems***43**(2)(2014), pp. 105–134.

WoS, Sco, GS Fuzzy and rough formal concept analysis: a survey.

In:*26th Fuzzy System Symposium*, 2010, pp. 1049–1054.

GS Hierarchical clustering methods using Formal Concept Analysis - The algorithms for single-link and complete-link clustering. *Dissertation Thesis*, 2010, 297 pp.

GS Essays on using formal concept analysis in information engineering. *Orthopaedics & Traumatology-Surgery & Research***95**(8)(2009), pp. S49–S59.

WoS Comparative anatomy of the knee joint: Effects on the lateral meniscus.

*Int. Journal of Foundations of Computer Science***19**(2)(2008), pp. 255–269.

[World Scientific, Fast factorization by similarity of fuzzy concept lattices with hedges. DOI 10.1142/S012905410800567X, ISSN 0129–0541]

IF: 0.554, DB: WoS (WOS:000255611400002), Sco, GS, RIVPDF | abstract | 13 citations (6 WoS, 9 Sco, 13 GS)**Abstract**The paper presents results on factorization by similarity of fuzzy concept lattices with hedges. A fuzzy concept lattice is a hierarchically ordered collection of clusters extracted from tabular data. The basic idea of factorization by similarity is to have, instead of a possibly large original fuzzy concept lattice, its factor lattice. The factor lattice contains less clusters than the original concept lattice but, at the same time, represents a reasonable approximation of the original concept lattice and provides us with a granular view on the original concept lattice. The factor lattice results by factorization of the original fuzzy concept lattice by a similarity relation. The similarity relation is specified by a user by means of a single parameter, called a similarity threshold. Smaller similarity thresholds lead to smaller factor lattices, i.e. to more comprehensible but less accurate approximations of the original concept lattice. Therefore, factorization by similarity provides a trade-off between comprehensibility and precision. We first describe the notion of factorization. Second, we present a way to compute the factor lattice directly from input data, i.e. without the need to compute the possibly large original concept lattice. Third, we provide an illustrative example to demonstrate our method.**Citations***International Journal of Applied Research on Information Technology and Computing***5**(1)(2014), pp. 14–24.

GS Information Retrieval: A Fuzzy Perspective. *International Journal of Advanced Intelligence Paradigms***6**(4)(2014), pp. 272–311.

Sco, GS Fuzzy information retrieval in WWW: a survey. *International Journal of General Systems***43**(2)(2014), pp. 105–134.

WoS, Sco, GS Fuzzy and rough formal concept analysis: a survey.

In: Pant M., Deep K., Nagar A., Bansal J. Ch. (Eds.):*Proceedings of the Third International Conference on Soft Computing for Problem Solving, SocProS 2013, Volume 2*,*Advances in Intelligent Systems and Computing***259**, 2013, pp. 433–445.

Sco, GS A Survey on Web Information Retrieval Inside Fuzzy Framework. *Information Systems Frontiers***15**(3)(2013), pp. 511–520.

WoS, Sco, GS Similarity reasoning for the semantic web based on fuzzy concept lattices: An informal approach. *Knowledge-Based Systems***26**(2012), pp. 40–47.

WoS, Sco, GS Semantic Web search based on rough sets and Fuzzy Formal Concept Analysis. *Fuzzy Sets and Systems***183**(1)(2011), pp. 1–25.

WoS, Sco, GS Extracting compact and information lossless sets of fuzzy association rules. *Dissertation Thesis*, 2010, 297 pp.

GS Essays on using formal concept analysis in information engineering. *International Journal of Uncertainty Fuzziness and Knowledge-Based Systems***18**(2)(2010), pp. 153–167.

WoS, Sco, GS Concept Similarity in Fuzzy Formal Concept Analysis for Semantic Web. *Annals of Mathematics and Artificial Intelligence***59**(2)(2010), pp. 187–200.

WoS, Sco, GS Factorization of fuzzy concept lattices with hedges by modification of input data.

2009, 17 pp.

GS Concept Similarity in Fuzzy Formal Concept Analysis for Semantic Web. *Computer Journal***51**(2)(2008), pp. 240–254.

GS Similarity of XML-schema Elements: A Structural and Information Content Approach.

In: Belohlavek R., Kuznetsov S. O. (Eds.):*Proc. CLA 2008*, 2008, pp. 231–241.

Sco, GS Factorization of Concept Lattices with Hedges by Means of Factorization of Residuated Lattices.

*Journal of Computer and System Sciences***73**(6)(2007), pp. 1012–1022.

[Elsevier, Fast factorization by similarity in formal concept analysis of data with fuzzy attributes. DOI 10.1016/j.jcss.2007.03.016, ISSN 0022–0000]

IF: 1.185, DB: WoS (WOS:000248133700011), Sco, GS, RIVPDF | abstract | 39 citations (17 WoS, 23 Sco, 36 GS) − 11 co-citations (7 WoS, 6 Sco, 10 GS) + 2 selfcitations (1 WoS, 1 Sco, 2 GS)**Abstract**We present a method of fast factorization in formal concept analysis (FCA) of data with fuzzy attributes. The output of FCA consists of a partially ordered collection of clusters extracted from a data table describing objects and their attributes. The collection is called a concept lattice. Factorization by similarity enables us to obtain, instead of a possibly large concept lattice, its factor lattice. The elements of the factor lattice are maximal blocks of clusters which are pairwise similar to degree exceeding a user-specified threshold. The factor lattice thus represents an approximate version of the original concept lattice. We describe a fuzzy closure operator the fixed points of whichare just clusters which uniquely determine the blocks of clusters of the factor lattice. This enables us to compute the factor lattice directly from the data without the need to compute the whole concept lattice. We present theoretical solution and examples demonstrating the speed-up of our method.**Citations**

In: Kóczy L., Medina J. (Eds.):*ESCIM 2016*, 2016, pp. 49–54.

GS Attribute Reduction in Fuzzy Formal Concept Analysis
from Rough Set Theory. *Fuzzy Sets and Systems*(2016).

GS An efficient method to factorize fuzzy attribute-oriented concept lattices.

In: Balcan M. F., Weinberger K. Q. (Eds.):*33rd International Conference on Machine Learning, ICML 2016*, 2016, pp. 1486–1499.

Sco, GS Boolean Matrix Factorization and Noisy Completion via Message Passing. *Journal of Computer and System Sciences***82**(2)(2016), pp. 357–365.

WoS, Sco, GS Bases of closure systems over residuated lattices. *Soft Computing***20**(4)(2016), pp. 1485–1502.

WoS, Sco, GS Knowledge representation using interval-valued fuzzy formal concept lattice. *Journal of Chemical Information and Modeling***55**(9)(2015), pp. 1781–1803.

Sco, GS Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis. *arXiv:1506.03930*, 2015, 24 pp.

GS Complete relations on fuzzy complete lattices. *Fuzzy Sets and Systems***275**(2015), pp. 88–109.

WoS, Sco, GS Similarity relations in fuzzy attribute-oriented concept lattices. *Dissertation Thesis*, 2014, 25 pp.

GS Metric and Topological Aspects in Distributed Systems. *International Journal of General Systems***43**(2)(2014), pp. 105–134.

WoS, Sco, GS Fuzzy and rough formal concept analysis: a survey.

2013.

Sco Strategic intelligence management: National security imperatives and information and communications technologies.

In: Pedrycz W., Reformat M. Z. (Eds.):*Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)*, 2013, pp. 1477–1482.

Sco, GS Developing type-2 fuzzy FCA for similarity reasoning in the semantic web. *Dissertation Thesis*, 2013, 234 pp.

GS Métrologie des graphes de terrain, application à la construction de ressources lexicales et à la recherche d'information.

In:*Machine Learning and Cybernetics (ICMLC), 2012 International Conference on (Volume:1)*, 2012, pp. 249–254.

Sco, GS Axiomatic approaches of fuzzy concept operators. *International Journal of Applied Mathematics and Computer Science***28**(8)(2011), pp. 67–70.

GS Semantic Web Service Matching Based on Fuzzy Concept Lattice. *American Journal of Engineering and ...*(2011).

GS Entropy-based attributes significance measure and its application in concept approximation.

In: Belohlavek R., Klir G. J. (Eds.):*Concepts and Fuzzy Logic*, 2011, pp. 177–207.

WoS Formal Concept Analysis: Classical and Fuzzy. *Artificial Intelligence in Medicine***51**(1)(2011), pp. 27–41.

WoS, Sco, GS Conceptual-driven classification for coding advise in health insurance reimbursement.

In: Kuznetsov S. O., Slezak D., Hepting D. H., Mirkin B. G. (Eds.):*Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGRC 2011*,*Lecture Notes in Artificial Intelligence***6743**, 2011, pp. 19–26.

WoS, Sco, GS What is a Fuzzy Concept Lattice? II.

2010

GS Separations of Formal Contexts.

In: Li M., Liang Q., Wang L., Song Y. (Eds.):*Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on (Volume:4 ), Proceedings*, 2010, pp. 1966–1970.

Sco, GS A structural information method for evaluating concept similarity.

In: Wierman M. (Ed.):*2010 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), Proceedings*, 2010, pp. 1–6.

Sco, GS Fuzzy concept lattices: Examples using the Gene Ontology. *Dissertation Thesis*, 2010, 297 pp.

GS Essays on using formal concept analysis in information engineering. *Applied Intelligence***33**(1)(2010), pp. 67–78.

WoS, Sco, GS Constructing tree-based knowledge structures from text corpus. *Journal of Computer and System Sciences***76**(1)(2010), pp. 3–20.

WoS, Sco, GS Discovery of optimal factors in binary data via a novel method of matrix decomposition.

In: Yu J., Greco S., Lingras P., Wang G., Skowron A. (Eds.):*Rough Set and Knowledge Technology (RSKT)*,*Lecture Notes in Artificial Intelligence***6401**, 2010, pp. 187–194.

WoS, Sco, GS Conceptual Reduction of Fuzzy Dual Concept Lattices. *Annals of Mathematics and Artificial Intelligence***59**(2)(2010), pp. 187–200.

WoS, Sco, GS Factorization of fuzzy concept lattices with hedges by modification of input data.

In: Chien B.-Ch., Hong T.-P., Chen S.-M., Ali M. (Eds.):*Next-Generation Applied Intelligence, 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009, Proceedings*,*Lecture Notes in Computer Science***5579**, 2009, pp. 439–448.

Sco, GS Heuristic-Based Approach for Constructing Hierarchical Knowledge Structures. *Information Sciences***179**(15)(2009), pp. 2656–2661.

WoS, Sco, GS Grouping fuzzy sets by similarity.

In: Zhang S. (Ed.):*International Symposium on Computer Science & Technology, Proceedings*, 2009, pp. 85–89.

WoS Semantic Web Service Matching Based on Fuzzy Concept Lattice. *Logic Journal of the IGPL***17**(2)(2009), pp. 205–223.

WoS, Sco, GS Factorization of residuated lattices.

In: Belohlavek R., Kuznetsov S. O. (Eds.):*Proc. CLA 2008*, 2008, pp. 231–241.

Sco, GS Factorization of Concept Lattices with Hedges by Means of Factorization of Residuated Lattices.

In: Galindo J. (Ed.):*Handbook of Research on Fuzzy Information Processing in Databases*, 2008, pp. 462–489.

GS Relational Data, Formal Concept Analysis, and Graded Attributes.

In: IEEE (Ed.):*2008 4th International IEEE Conference Intelligent Systems, Vols 1 and 2*, 2008, pp. 634–637.

WoS, Sco, GS Factor structures and central points by similarity.

In: IEEE (Ed.):*2008 4th International IEEE Conference Intelligent Systems, Vols 1 and 2*, 2008, pp. 628–633.

WoS, Sco, GS Optimal decompositions of matrices with grades.

In: ATlantic PRess (Ed.):*Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)*,*Advances in Intelligent Systems Research*, 2007.

WoS, GS Linguistic truth-valued concept lattice based on lattice-valued logic.

In: Last M., Szczepaniak P. S., Volkovich Z., Kandel A. (Eds.):*Advances in Web Intelligence and Data Mining*, 2006, pp. 243–252.

GS Estimations of Similarity in Formal Concept Analysis of Data with Graded Attributes.

In: Zhang D., Khoshgoftaar T. M., Joshi J. B. D. (Eds.):*Proceedings of the 2006 IEEE International Conference on Information Reuse and Integration (IRI - 2006)*, 2006, pp. 132–135.

GS Similarity issues in attribute implications from data with fuzzy attributes.

GS Geometry and heuristics for discovery of optimal factors in binary data.

In: Ben Yahia S., Mephu Nguifo E., Belohlavek R. (Eds.):*Concept Lattices and their Applications*,*Lecture Notes in Artificial Intelligence***4923**, 2008, pp. 68–79.

WoS, Sco, GS Direct factorization by similarity of fuzzy concept lattices by factorization of input data. *Dissertation Thesis*, 2006, 77 pp.

GS Factorizing Fuzzy Concept Lattices by Similarity.

*Journal of Electrical Engineering***56**(12/s)(2005), pp. 41–45.

[Slovak University of Technology, ISSN 1335–3632]

DB: Similarity clarification in formal concept analysis. RIVabstract | 1 selfcitation (1 GS)**Abstract**Formal concept analysis (FCA) is an algebraic method of data miningwhich aims at extracting a hierarchical structure (so-called conceptlattice) of clusters (so-called formal concepts) from object-attributedata tables. One of the hottest problems in application of FCA is alarge number of clusters extracted from data. We try to cope withthis problem by reducing the amount of input data by the well-knownmethod called clarification, extended to fuzzy setting. This reductionhas the effect of clustering of similar formal concepts and therebymakes the concept lattice smaller.**Citations***Dissertation Thesis*, 2006, 77 pp.

GS Factorizing Fuzzy Concept Lattices by Similarity.

## Papers in conference proceedings

In: Zhao Y., Islam M. Z., Stone G., Ong K.-L., Sharma D., Williams G. (Eds.):*Proceedings of the the Fourteenth Australasian Data Mining Conference, AusDM 2016, Conferences in Research and Practice in Information Technology, Vol. 170*, 2016, pp. 71–79, Canberra, Australia, 12/2016.

[Australian Computer Society] Running Boolean Matrix Factorization in Parallel.PDF | abstract**Abstract**Boolean matrix factorization (also known as Boolean matrix decomposition) is a well established method for analysis and preprocessing of data. There is a number of various algorithms for Boolean matrix factorization, but none of them uses benefits of parallelization. This is mainly due to the fact that the algorithms utilize greedy heuristics that are inherently sequential. In this work, we propose a general parallelization scheme—and an algorithm which uses it—for Boolean matrix factorization. Our approach computes several possible locally most optimal (from heuristic perspective) partial decompositions and constructs several most optimal final decompositions in more processes running simultaneously in parallel. As a result of the computation, either the single most optimal decomposition or several top-k of them can be returned. The approach could be applied to any sequential heuristic Boolean matrix factorization algorithm. Moreover, we present results of various experiments involving this new algorithm on synthetic and real datasets.

In: Brejová B. (Ed.):*Proceedings of the 16th ITAT Conference Information Technologies - Applications and Theory, ITAT 2016, Workshop on Computational Intelligence and Data Mining, WCIDM 2016*, 2016, pp. 147–154, Tatranské Matliare, Slovakia, 9/2016.

[CreateSpace Independent Publishing Platform, Evaluating Association Rules in Boolean Matrix Factorization. CEUR WS, Vol. 1649, ISBN 978-1537016740]

DB: Sco, GSPDF | abstract | 1 citation (1 GS) − 1 co-citation (1 GS)**Abstract**Association rules, or association rule mining, is a well-established and popular method of data mining and machine learning successfully applied in many different areas since mid-nineties. Association rules form a ground of the Asso algorithm for discovery of the first (presumably most important) factors in Boolean matrix factorization. In Asso, the confidence parameter of association rules heavily influences the quality of factorization. However, association rules, in a more general form, appear already in GUHA, a knowledge discovery method developed since mid-sixties. In the paper, we evaluate the use of various (other) types of association rules from GUHA in Asso and, from the other side, a possible utilization of (particular) association rules in other Boolean matrix factorization algorithms not based on the rules. We compare the quality of factorization produced by the modified algorithms with those produced by the original algorithms.**Citations***Dissertation Thesis*, 2016

GS Decompositions of matrices with relational data: foundations and algorithms.

In: Yager R., Sgurev V., Hadjiski M., Jotsov V. (Eds.):*Proceedings of the IEEE 8th International Conference on Intelligent Systems, IS 2016*, 2016, pp. 227–233, Sofia, Bulgaria, 9/2016.

[IEEE, How to assess quality of BMF algorithms?. DOI 10.1109/IS.2016.7737426, ISBN 978-1-5090-1353-8]

DB: WoS (WOS:000391554300032), Sco, GSPDF | abstract | 1 citation (1 GS) − 1 co-citation (1 GS)**Abstract**We critically examine the problem of quality assessment of algorithms for Boolean matrix factorization. We argue that little attention is paid to this problem in the literature. We view this problem as a multifaceted one and identify key aspects with respect to which the quality of algorithms should be assessed. Because of its utmost importance, we focus on assessment of quality of sets of factors extracted from Boolean data, propose ways to assess such quality and provide experimental evaluation involving selected factorization algorithms. We argue that the views involved in our proposal, represent reasonable basic standpoints for further systematic approaches to quality assessment.**Citations***Dissertation Thesis*, 2016

GS Decompositions of matrices with relational data: foundations and algorithms.

In: Huchard M., Kuznetsov S. O. (Eds.):

[National Research University Higher School of Economics, Moscow, Russia, Scalable Performance of FCbO Update Algorithm on Museum Data. CEUR WS, Vol. 1624, ISBN 978-5-600-01454-1]

DB: GSPDF | abstract**Abstract**Formal Concept Analysis – known as a technique for data analysis and visualisation – can also be applied as a means of creating interaction approaches that allow for knowledge discovery within collections of content. These interaction approaches rely on performant algorithms that can generate conceptual neighbourhoods based on a single formal concept, or incrementally compute and update a set of formal concepts given changes to a formal context. Using case studies based on content from museum collections, this paper describes the scalability limitations of existing interaction approaches and presents an implementation and evaluation of the FCbO update algorithm as a means of updating formal concepts from large and dynamically changing museum datasets.

In: Ojeda-Aciego M., Outrata J. (Eds.):*CLA 2013: Proceedings of the 10th International Conference on Concept Lattices and Their Applications*, 2013, pp. 261–274, La Rochelle, France, 10/2013.

[Laboratory L3i, University of La Rochelle, La Rochelle, France, A lattice-free concept lattice update algorithm based on *CbO. CEUR WS, Vol. 1062, ISBN 978–2–7466–6566–8]

DB: Sco, GS, RIVPDF | abstract | 3 citations (3 Sco, 3 GS) + 1 selfcitation (1 GS)**Abstract**Updating a concept lattice when introducing new objects to input data can be done by any of the so-called incremental algorithms for computing concept lattice of the data. The algorithms use and update the lattice while introducing new objects one by one. The present concept lattice of input data without the new objects is thus required before the update. In this paper we propose an efficient algorithm for updating the lattice from the present and new objects only, not requiring the possibly large concept lattice of present objects. The algorithm results as a modification of the CbO algorithm for computing the set of all formal concepts, or its modifications like FCbO, PCbO or PFCbO, to compute new and modified formal concepts only and the changes of the lattice order relation when input data changes. We describe the algorithm and present an experimental evaluation of its performance and a comparison with AddIntent incremental algorithm for computing concept lattice.**Citations**

In: Ojeda-Aciego M., Lepskiy A., Ignatov D.I. (Eds.):*2nd International Workshop on Soft Computing Applications and Knowledge Discovery, SCAKD 2016*, 2016, pp. 51–62.

Sco, GS Modification of good tests in dynamic contexts: Application to modeling intellectual development of cadets. *Expert Systems with Applications***42**(9)(2015), pp. 4474–4481.

WoS, Sco, GS A fast incremental algorithm for constructing concept lattices.

In: Bertet K., Rudolph S. (Eds.):*Proc. CLA 2014*, 2014, pp. 195–207.

Sco, GS Removing an incidence from a formal context.

In: Huchard M., Kuznetsov S. O. (Eds.):

GS Scalable Performance of FCbO Update Algorithm on Museum Data.

In: Szathmary L., Priss U. (Eds.):*CLA 2012: Proceedings of the 9th International Conference on Concept Lattices and Their Applications*, 2012, pp. 305–316, Fuengirola (Málaga), Spain, 10/2012.

[Universidad de Málaga, Málaga, Spain, Impact of Boolean factorization as preprocessing methods for classification of Boolean data. CEUR WS, Vol. 972, ISBN 978–84–695–5252–0]

DB: Sco, RIVPDF | abstract | 1 citation (1 Sco, 1 GS)**Abstract**The paper explores a utilization of Boolean factorization as a method for data preprocessing in classification of Boolean data. In previous papers, we demonstrated that data preprocessing consisting in replacing the original Boolean attributes by factors, i.e. new Boolean attributes that are obtained from the original ones by Boolean factorization, improves the quality of classification. The aim of this paper is to explore the question of how the various Boolean factorization methods that were proposed in the literature impact the quality of classification. In particular, we compare three factorization methods, present experimental results, and outline issues for future research.**Citations**

In:*Proceedings of 2014 IEEE International Conference on Behavior, Economic and Social Computing (BESC 2014)*, 2014, pp. 1–6.

Sco, GS Maximum likelihood estimation based DINA model and Q-matrix learning.

In: Cook D., Pei J., Wang W., Zaiane O., Wu X. (Eds.):*Proceedings of the ICDM 2011, The 11th IEEE International Conference on Data Mining*, 2011, pp. 1128–1133, Vancouver, Canada, 12/2011.

[IEEE Computer Society, Conference Publishing Services, Los Alamitos, California, USA, Using frequent closed itemsets for data dimensionality reduction. DOI 10.1109/ICDM.2011.154, ISBN 978–0–7695–4408–3]

DB: GS, RIVabstract | 6 citations (6 GS)**Abstract**We address important issues of dimensionality reduction of transactional data sets where the input data consists of lists of transactions, each of them being a finite set of items. The reduction consists in finding a small set of new items, so-called factor-items, which is considerably smaller than the original set of items while comprising full or nearly full information about the original items. Using this type of reduction, the original data set can be represented by a smaller transactional data set using factor-items instead of the original items, thus reducing its dimensionality. The procedure utilized in this paper is based on approximate Boolean matrix decomposition. In this paper, we focus on the role of frequent closed itemsets that can be used to determine factor-items. We present the factorization problem, its reduction to Boolean matrix decompositions, experiments with publicly available data sets, and an algorithm for computing decompositions.**Citations***Concurrency and Computation, Practice and Experience*(2016).

GS A new closed frequent itemset mining algorithm based on GPU and improved vertical structure.

In:*Data Science and Advanced Analytics (DSAA), 2014 International Conference on*, 2014, pp. 39–45.

GS Itemset approximation using Constrained Binary Matrix Factorization.

In: Özcan E., Burke E. K., McCollum B. (Eds.):*10th International Conference of the Practice and Theory of Automated Timetabling (PATAT 2014), Proceedings*, 2014, pp. 549–553.

GS Timetabling in Higher Education: Considering the Combinations of Classes Taken by Students.

In: Luo X., Xu Yu J., Li Z. (Eds.):*Advanced Data Mining and Applications, 10th International Conference, ADMA 2014, Proceedings*,*Lecture Notes in Computer Science***8933**, 2014, pp. 1–15.

GS A New Improved Apriori Algorithm Based on Compression Matrix.

In:*Web Information System and Application Conference (WISA), 2013 10th, Proceedings*, 2013, pp. 440–445.

GS An Incremental Closed Frequent Itemsets Mining Algorithm Based on Shadow Prefix Tree.

GS Implementing and analysing dataset dimensionality reduction through frequent closed itemsets.

In: Napoli A., Vychodil V. (Eds.):*CLA 2011: Proceedings of the 8th International Conference on Concept Lattices and Their Applications*, 2011, pp. 207–222, Nancy, France, 10/2011.

[INRIA Nancy - Grand Est and LORIA, Nancy, France, Boolean factors as a means of clustering of interestingness measures of association rules. CEUR WS, Vol. 959, ISBN 978–2–905267–78–8]

DB: Sco, RIVPDF | abstract**Abstract**Measures of interestingness play a crucial role in association rule mining. An important methodological problem is to provide a reasonable classification of the measures. Several papers appeared on this topic. In this paper, we explore Boolean factor analysis, which uses formal concepts corresponding to classes of measures as factors, for the purpose of classification and compare the results to the previous approaches.

In: Draghici S., Khoshgoftaar T. M., Palade V., Pedrycz V., Wani M. A., Zhu X. (Eds.):*Proceedings of The Ninth Int. Conf. on Machine Learning and Applications (ICMLA 2010)*, 2010, pp. 899–902, Washington, D.C., USA, 12/2010.

[IEEE, Boolean factor analysis for data preprocessing in machine learning. DOI 10.1109/ICMLA.2010.141, ISBN 978–0–7695–4300–0]

DB: Sco, GS, RIVabstract | 14 citations (2 WoS, 10 Sco, 14 GS) + 2 selfcitations (2 Sco, 2 GS)**Abstract**We present two input data preprocessing methods for machine learning (ML). The first one consists in extending the set of attributes describing objects in input data table by new attributes and the second one consists in replacing the attributes by new attributes. The methods utilize formal concept analysis (FCA) and boolean factor analysis, recently described by FCA, in that the new attributes are defined by so-called factor concepts computed from input data table. The methods are demonstrated on decision tree induction. The experimental evaluation and comparison of performance of decision trees induced from original and preprocessed input data is performed with standard decision tree induction algorithms ID3 and C4.5 on several benchmark datasets.**Citations**

In:*1st International Workshop on Clustering High-Dimensional Data, CHDD 2012*,*Lecture Notes in Computer Science***7627**, 2015, pp. 118–133.

Sco, GS Dimensionality reduction in boolean data: Comparison of four BMF methods.

In:*8th Russian Summer School on Information Retrieval, RuSSIR 2014*,*Communications in Computer and Information Science***505**, 2015, pp. 42–141.

WoS, Sco, GS Introduction to formal concept analysis and its applications in information retrieval and related fields. *Journal of Computer and System Sciences***81**(8)(2015), pp. 1678–1697.

Sco, GS From-below approximations in Boolean matrix factorization: Geometry and new algorithm. *Machine Learning***101**(1-3)(2015), pp. 271–302.

WoS, GS Triadic Formal Concept Analysis and triclustering: searching for optimal patterns. *International Journal of General Systems***43**(5)(2014), pp. 521–534.

Sco, GS Computing minimal sets of descriptive conditions for binary data. *arXiv:1306.4905*, 2013, 38 pp.

GS From-Below Approximations in Boolean Matrix Factorization: Geometry and New Algorithm. *Order***30**(2)(2013), pp. 437–454.

Sco, GS Optimal Factorization of Three-Way Binary Data Using Triadic Concepts. *Expert Systems with Applications***40**(16)(2013), pp. 6601–6623.

WoS, Sco, GS Formal Concept Analysis in knowledge processing: A survey on models and techniques. *Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery***3**(3)(2013), pp. 200–215.

WoS, Sco, GS Knowledge representation and processing with formal concept analysis.

In: Liu B., Ma M., Chang J. (Eds.):*Information Computing and Applications, Third International Conference, ICICA 2012, Proceedings*,*Lecture Notes in Computer Science***7473**, 2012, pp. 492–499.

Sco, GS Study on Data Preprocessing for Daylight Climate Data.

In:*Communication Technology (ICCT), 2012 IEEE 14th International Conference on , Proceedings*, 2012, pp. 317–323.

Sco, GS A Layered Recursive Construction Algorithm and a visualization method for concept lattice. *Dissertation Thesis*, 2012, 172 pp.

GS Conceptual Factors and Fuzzy Data. *Dissertation Thesis*, 2012, 77 pp.

GS Concept analysis of three-way ordinal matrices.

In: Christiansen H., De Tré G., Yazici A., Zadrozny S., Andreasen T., Larsen H. L. (Eds.):*Flexible Query Answering Systems, 9th International Conference, FQAS 2011, Proceedings*,*Lecture Notes in Computer Science***7022**, 2011, pp. 400–411.

Sco, GS Factorizing Three-Way Ordinal Data Using Triadic Formal Concepts. *Annals of Mathematics and Artificial Intelligence***72**(1–2)(2014), pp. 3–22.

WoS, Sco, GS Impact of Boolean factorization as preprocessing methods for classification of Boolean data.

In: Szathmary L., Priss U. (Eds.):*CLA 2012: Proceedings of the 9th International Conference on Concept Lattices and Their Applications*, 2012, pp. 305–316.

Sco Impact of Boolean factorization as preprocessing methods for classification of Boolean data.

In: Kryszkiewicz M., Obiedkov S. (Eds.):*CLA 2010: Proceedings of the 7th International Conference on Concept Lattices and Their Applications*, 2010, pp. 187–198, Sevilla, Spain, 10/2010.

[University of Sevilla, Sevilla, Spain, Preprocessing input data for machine learning by FCA. CEUR WS, Vol. 672, ISBN 978–84614–4027–6]

DB: Sco, GS, RIVPDF | abstract | 6 citations (6 GS) + 2 selfcitations (2 Sco, 2 GS)**Abstract**The paper presents an utilization of formal concept analysis in input data preprocessing for machine learning. Two preprocessing methods are presented. The first one consists in extending the set of attributes describing objects in input data table by new attributes and the second one consists in replacing the attributes by new attributes. In both methods the new attributes are defined by certain formal concepts computed from input data table. Selected formal concepts are so-called factor concepts obtained by boolean factor analysis, recently described by FCA. The ML method used to demonstrate the ideas is decision tree induction. The experimental evaluation and comparison of performance of decision trees induced from original and preprocessed input data is performed with standard decision tree induction algorithms ID3 and C4.5 on several benchmark datasets.**Citations***Knowledge-Based Systems***99**(2016), pp. 92–102.

GS The attribute reductions of three-way concept lattices. *Dissertation Thesis*, 2016

GS Decompositions of matrices with relational data: foundations and algorithms. *International Journal of Engineering and Techniques***1**(3)(2015).

GS Pre-processing data using ID3 classifier. *Information Sciences***279**(2014), pp. 512–527.

GS Triadic concept lattices in the framework of aggregation structures. *Dissertation Thesis*, 2012, 77 pp.

GS Concept analysis of three-way ordinal matrices.

In: Christiansen H., De Tré G., Yazici A., Zadrozny S., Andreasen T., Larsen H. L. (Eds.):*Flexible Query Answering Systems, 9th International Conference, FQAS 2011, Proceedings*,*Lecture Notes in Computer Science***7022**, 2011, pp. 400–411.

GS Factorizing Three-Way Ordinal Data Using Triadic Formal Concepts. *Annals of Mathematics and Artificial Intelligence***72**(1–2)(2014), pp. 3–22.

WoS, Sco, GS Impact of Boolean factorization as preprocessing methods for classification of Boolean data.

In: Szathmary L., Priss U. (Eds.):*CLA 2012: Proceedings of the 9th International Conference on Concept Lattices and Their Applications*, 2012, pp. 305–316.

Sco Impact of Boolean factorization as preprocessing methods for classification of Boolean data.

In: Kryszkiewicz M., Obiedkov S. (Eds.):*CLA 2010: Proceedings of the 7th International Conference on Concept Lattices and Their Applications*, 2010, pp. 325–337, Sevilla, Spain, 10/2010.

[University of Sevilla, Sevilla, Spain, Advances in algorithms based on CbO. CEUR WS, Vol. 672, ISBN 978–84614–4027–6]

DB: Sco, GS, RIVPDF | abstract | 30 citations (30 GS) + 5 selfcitations (3 Sco, 5 GS)**Abstract**The paper presents a survey of recent advances in algorithms for computing all formal concepts in a given formal context which result as modifications or extensions of CbO. First, we present an extension of CbO, so called FCbO, and an improved canonicity test that significantly reduces the number of formal concepts which are computed multiple times. Second, we outline a parallel version of the proposed algorithm and discuss various scheduling strategies and their impact on the overall performance and scalability of the algorithm. Third, we discuss important data preprocessing issues and their influence on the algorithms. Namely, we focus on the role of attribute permutations and present experimental observations about the efficiency of the proposed algorithms with respect to the number of inversions in such permutations.**Citations***Biologically Inspired Cognitive Architectures*(2017).

GS Behavior planning of intelligent agent with sign world model. *International Journal of Conceptual Structures and Smart Applications***4**(1)(2016).

GS Comparision Between Features of CbO based Algorithms for Generating Formal Concepts.

In: Huchard M., Kuznetsov S. O. (Eds.):*Proc. CLA 2016*, 2016, pp. 21–31.

GS Comparing Algorithms for Computing Lower Covers of Implication-closed Sets. *Mathematical Methods in the Applied Sciences*(2016).

GS Data mining algorithms to compute mixed concepts with negative attributes: an application to breast cancer data analysis. *Dissertation Thesis*, 2015, 159 pp.

GS Interactive Knowledge Discovery over Web of Data. *Dissertation Thesis*, 2015, 193 pp.

GS Formal Concept Analysis and Pattern Structures for mining Structured Data. *Automatic Documentation and Mathematical Linguistics***49**(5)(2015), pp. 153–162.

GS WEBCHEM-JSM: an information environment for implementation of the JSM method in pharmacology. *Automatic Documentation and Mathematical Linguistics***49**(4)(2015), pp. 109–116.

GS Selection of an algorithm for the parallel implementation of the similarity method in intelligent DSM systems. *Information Sciences***295**(2015), pp. 633–649.

WoS, GS A ‘Best-of-Breed’ approach for designing a fast algorithm for computing fixpoints of Galois Connections. *Dissertation Thesis*, 2014

GS Intelligent Data Mining on Large-scale Heterogeneous Datasets and its Application in Computational Biology. *Journal of Engineering Science and Technology Review***7**(1)(2014), pp. 1–8.

GS Induction of formal concepts by lattice computing techniques for tunable classification. *Dissertation Thesis*, 2014

GS On the enumeration of pseudo-intents : choosing the order and extending to partial implications.

In:*КИИ-2014, Том 2*, 2014, pp. 274–282.

GS ПОВЫШЕНИЕ БЫСТРОДЕЙСТВИЯ ДСМ-МЕТОДА В ЗАДАЧАХ ОБРАБОТКИ ТЕКСТОВОЙ ИНФОРМАЦИИ. *arXiv:1403.3562v3*, 2014, 35 pp.

GS Enumerating all maximal biclusters in numerical datasets. *Procedia Computer Science***31**(2014), pp. 918–927.

GS Is Concept Stability a Measure for Pattern Selection?.

In: Glodeanu C. V., Kaytoue M., Sacarea Ch. (Eds.):*Formal Concept Analysis, 12th International Conference, ICFCA 2014. Proceedings*,*Lecture Notes in Computer Science***8478**, 2014, pp. 157–172.

GS Scalable Estimates of Concept Stability. *Research Report*, 2014, 28 pp.

GS Lattice-Based View Access: A way to Create Views over SPARQL Query for Knowledge Discovery.

In: IEEE (Ed.):*2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)*, 2014, pp. 61–68.

GS FCknn: A granular knn classifier based on formal concepts.

In: Hernandez N., Jäschke R., Croitoru M. (Eds.):*Graph-Based Representation and Reasoning, 21st International Conference on Conceptual Structures, ICCS 2014. Proceedings*,*Lecture Notes in Artificial Intelligence***8577**, 2014, pp. 37–50.

WoS, GS A Partial-Closure Canonicity Test to Increase the Efficiency of CbO-Type Algorithms.

In:*Proceedings of The 2013 World Congress in Computer Science, Computer Engineering, and Applied Computing*, 2013.

GS Hierarchical Classification Using FCA and the Cosine Similarity Function. *ResearchGate*, 2013

GS A method for improving Algorithms of Formal Concepts extraction using Prime Numbers.

In: Ojeda-Aciego M., Outrata J. (Eds.):*Proc. CLA 2013*, 2013, pp. 299–304.

GS Comparing Performance of Formal Concept Analysis and Closed Frequent Itemset Mining Algorithms on Real Data. *Dissertation Thesis*, 2013, 91 pp.

GS Redes Sociais e Classificação Conceptual: Abordagem Complementar para um sistema de Recomendação de Coautorias. *Journal of Advanced Computational Intelligence and Intelligent Informatics***17**(5)(2013), pp. 761–771.

GS An Algorithm for Recomputing Concepts in Microarray Data Analysis by Biological Lattice. *Expert Systems with Applications***40**(16)(2013), pp. 6601–6623.

WoS, GS Formal Concept Analysis in knowledge processing: A survey on models and techniques. *Dissertation Thesis*, 2012

GS Classificação hierárquica utilizando análise formal de conceitos. *Dissertation Thesis*, 2012, 77 pp.

GS Concept analysis of three-way ordinal matrices.

In: Domenach F., Ignatov D. I., Poelmans J. (Eds.):*Formal Concept Analysis, 10th International Conference, ICFCA 2012. Proceedings*,*Lecture Notes in Computer Science***7278**, 2012, pp. 164–179.

GS Formal Concept Discovery in Semantic Web Data.

In: Valtchev P., Jäschke R. (Eds.):*Formal Concept Analysis, 9th International Conference, ICFCA 2011. Proceedings*,*Lecture Notes in Computer Science***6628**, 2011, pp. 135–150.

GS Biclustering Numerical Data in Formal Concept Analysis.

In: Andrews S., Polovina S., Hill R., Akhgar B. (Eds.):*Conceptual Structures for Discovering Knowledge, 19th International Conference on Conceptual Structures, ICCS 2011*,*Lecture Notes in Computer Science***6828**, 2011, pp. 50–62.

GS In-Close2, a High Performance Formal Concept Miner.

In: Huchard M., Kuznetsov S. O. (Eds.):

GS Scalable Performance of FCbO Update Algorithm on Museum Data. *Int. Journal of General Systems***45**(2)(2016), pp. 211–231.

WoS, Sco, GS A lattice-free concept lattice update algorithm.

In: Ojeda-Aciego M., Outrata J. (Eds.):

Sco, GS A lattice-free concept lattice update algorithm based on *CbO. *Fundamenta Informaticae***115**(4)(2012), pp. 395–417.

WoS, Sco, GS Computing formal concepts by attribute sorting. *Information Sciences***185**(1)(2012), pp. 114–127.

WoS, Sco, GS Fast Algorithm for Computing Fixpoints of Galois Connections Induced by Object-Attribute Relational Data.

In: Belohlavek R., Kuznetsov S. O. (Eds.):*CLA 2008: Proceedings of the Sixth International Conference on Concept Lattices and Their Applications*, 2008, pp. 71–82, Olomouc, Czech Rep., 10/2008.

[Palacký University, Olomouc, Czech Rep., Parallel Recursive Algorithm for FCA. CEUR WS, Vol. 433, ISBN 978–80–244–2111–7]

DB: Sco, GS, RIVPDF | abstract | 53 citations (2 Sco, 53 GS) − 1 co-citation (1 GS) + 1 selfcitation (1 Sco, 1 GS)**Abstract**This paper presents a parallel algorithm for computing formal concepts. Presented is a sequential version upon which we build the parallel one.We describe the algorithm, its implementation, scalability, and provide an initial experimental evaluation of its efficiency. The algorithm is fast, memory efficient, and can be optimized so that all critical operations are reduced to low-level bit-array operations. One of the key features of the algorithm is that it avoids synchronization which has positive impacts on its speed and implementation.**Citations***International Journal of Conceptual Structures and Smart Applications***4**(1)(2016).

GS Comparision Between Features of CbO based Algorithms for Generating Formal Concepts.

In: Hussain A. (Ed.):*Proc. 5th International Conference on Electronics, Communications and Networks (CECNet 2015)*,*Lecture Notes in Electrical Engineering***382**, 2016, pp. 251–262.

GS Modeling of Spatial-Temporal Associations on a Mobile Trajectory.

In: Andrews S., Polovina S. (Eds.):*Proc. Fifth Conceptual Structures Tools & Interoperability Workshop (CSTIW 2016), 22nd International Conference on Conceptual Structures (ICCS 2016)*, 2016, pp. 1–9.

Sco, GS A Tool for Creating and Visualising Formal Concept Trees. *The Journal of Supercomputing*(2016), pp. 1–13.

GS Reducing the search space by closure and simplification paradigms, A parallel key finding method. *Artificial Intelligence Research***5**(2)(2016).

GS Parallelization of the next Closure algorithm for generating the minimum set of implication rules. *Mathematical Methods in the Applied Sciences*(2016).

GS Data mining algorithms to compute mixed concepts with negative attributes: an application to breast cancer data analysis. *International Journal of Applied Mathematics and Computer Science***26**(2)(2016), pp. 495–516.

WoS, GS A Comprehensive Survey on Formal Concept Analysis, its Research Trends and Applications. *Automatic Documentation and Mathematical Linguistics***49**(5)(2015), pp. 153–162.

GS WEBCHEM-JSM: an information environment for implementation of the JSM method in pharmacology. *Automatic Documentation and Mathematical Linguistics***49**(4)(2015), pp. 109–116.

GS Selection of an algorithm for the parallel implementation of the similarity method in intelligent DSM systems. *Dissertation Thesis*, 2015, 245 pp.

GS Interrogation d'un réseau sémantique de documents: l'intertextualité dans l'accés á l'information juridique.

In: Ben Yahia S., Konecny J. (Eds.):*Proc. CLA 2015*, 2015, pp. 181–192.

GS NextClosures: Parallel Computation of the Canonical Base.

In: Carrasco-Ochoa J. A., Martínez-Trinidad J. F., Sossa-Azuela J. H., Olvera López J. A., Famili F. (Eds.):*Pattern Recognition, 7th Mexican Conference, MCPR 2015, Proceedings*,*Lecture Notes in Computer Science***9116**, 2015, pp. 236–245.

GS Patterns Used to Identify Relations in Corpus Using Formal Concept Analysis. *Doctoral Thesis*, 2015, 107 pp.

GS Makine çevirisinde yeni bir bilgisayımsal yaklaşım. *Engineering Letters***23**(2)(2015).

GS Identification of Ontological Relations in Domain Corpus Using Formal Concept Analysis. *Information Sciences***295**(2015), pp. 633–649.

WoS, GS A ‘Best-of-Breed’ approach for designing a fast algorithm for computing fixpoints of Galois Connections.

In: Acosta Guadarrama J. C., De Ita Luna G., Marcial Romero R., Osorio M., Zepeda C. (Eds.):*Proceedings of the Ninth Latin American Workshop on Logic/Languages, Algorithms and New Methods of Reasoning, LANMR 2014*, 2014.

GS Identification of Ontological Relations Using Formal Concept Analysis.

In: Glodeanu C. V., Kaytoue M., Sacarea Ch. (Eds.):*Formal Concept Analysis, 12th International Conference, ICFCA 2014. Proceedings*,*Lecture Notes in Computer Science***8478**, 2014, pp. 251–267.

GS Multilayered, Blocked Formal Concept Analyses for Adaptive Image Compression.

In: Hernandez N., Jäschke R., Croitoru M. (Eds.):*Graph-Based Representation and Reasoning, 21st International Conference on Conceptual Structures, ICCS 2014. Proceedings*,*Lecture Notes in Artificial Intelligence***8577**, 2014, pp. 37–50.

WoS, GS A Partial-Closure Canonicity Test to Increase the Efficiency of CbO-Type Algorithms. *Journal of Artificial Intelligence and Soft Computing Research***3**(3)(2013), pp. 189–200.

GS An Nlp-Based Approach for Improving Human-Robot Interaction.

In: Fred A., Dietz J. L. G., Liu K., Filipe J. (Eds.):*Knowledge Discovery, Knowledge Engineering and Knowledge Management, 4th International Joint Conference, IC3K 2012. Revised Selected Papers*,*Communications in Computer and Information Science***415**, 2013, pp. 260–270.

GS Interactive Exploration of Structural Concepts in Code.

In:*The 34th International Conference on Information Systems (ICIS 2013)*, 2013.

GS Pathways through Information Landscapes: Alternative Design Criteria for Digital Art Collections. *Information Sciences***236**(2013), pp. 66–82.

GS Formal concept analysis based on the topology for attributes of a formal context. *Evolving Systems***4**(3)(2013), pp. 183–193.

GS The transformation method between tree and lattice for file management system. *Journal of Advanced Computational Intelligence and Intelligent Informatics***17**(5)(2013), pp. 761–771.

GS An Algorithm for Recomputing Concepts in Microarray Data Analysis by Biological Lattice.

In:*The 17th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2013*, 2013.

GS Using Formal Concept Analysis for Categorizing Earth Science Data and Object Collections.

In: Bagchi A., Ray I. (Eds.):*Information Systems Security, 9th International Conference, ICISS 2013. Proceedings*,*Lecture Notes in Computer Science***8303**, 2013, pp. 133–147.

GS Policy Mining: A Bottom-Up Approach toward a Model Based Firewall Management.

In:*Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2013 International Conference on*, 2013, pp. 259–265.

GS Diagnosis Rule Mining of Airborne Avionics Using Formal Concept Analysis. *International Journal of Distributed Systems and Technologies (IJDST)***4**(2)(2013).

GS Discovering Knowledge in Data Using Formal Concept Analysis.

In: Cellier P., Distel F., Ganter B. (Eds.):*Formal Concept Analysis, 11th International Conference, ICFCA 2013. Proceedings*,*Lecture Notes in Computer Science***7880**, 2013, pp. 92–108.

GS Formal Concept Analysis via Atomic Priming. *International Journal of Education and Development using ICT***7**(3)(2012), pp. 50–73.

GS An approach to modeling ICT educational policies in African countries. *Dissertation Thesis*, 2012, 77 pp.

GS Concept analysis of three-way ordinal matrices.

In: Exman I., Llorens J., Fraga A. (Eds.):*3rd International Workshop on Software Knowledge - SKY 2012*, 2012.

GS Assisted Software Exploration using Formal Concept Analysis.

In: Yao JT., Yang Y., Słowiński R., Greco S., Li H., Mitra S., Polkowski L. (Eds.):*Rough Sets and Current Trends in Computing, 8th International Conference, RSCTC 2012. Proceedings*,*Lecture Notes in Computer Science***7413**, 2012, pp. 297–302.

GS A CUDA-Based Algorithm for Constructing Concept Lattices. *International Journal of Space-Based and Situated Computing***2**(2)(2012), pp. 123–138.

GS Knowledge discovery through creating formal contexts.

In: Domenach F., Ignatov D. I., Poelmans J. (Eds.):*Formal Concept Analysis, 10th International Conference, ICFCA 2012. Proceedings*,*Lecture Notes in Computer Science***7278**, 2012, pp. 77–95.

GS Publication Analysis of the Formal Concept Analysis Community.

In: Domenach F., Ignatov D. I., Poelmans J. (Eds.):*Formal Concept Analysis, 10th International Conference, ICFCA 2012. Proceedings*,*Lecture Notes in Computer Science***7278**, 2012, pp. 292–308.

GS Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework.

In: Domenach F., Ignatov D. I., Poelmans J. (Eds.):*Formal Concept Analysis, 10th International Conference, ICFCA 2012. Proceedings*,*Lecture Notes in Computer Science***7278**, 2012, pp. 164–179.

GS Formal Concept Discovery in Semantic Web Data. *Journal on Satisfiability, Boolean Modeling and Computation***8**(2012), pp. 63–82.

GS Compactly generating all satisfying truth assignments of a Horn formula.

In:*The 10th International Semantic Web Conference, ISWC 2011*, 2011.

GS Beyond RDF Links – Exploring the Semantic Web with the Help of Formal Concepts. *Master Thesis*, 2011, 97 pp.

GS Distributed Algorithms for Computing Closed Itemsets Based on an Iterative MapReduce Framework.

In: Napoli A., Vychodil V. (Eds.):*Proc. CLA 2011*, 2011, pp. 59–73.

GS Filtering Machine Translation Results with Automatically Constructed Concept Lattices.

In: Napoli A., Vychodil V. (Eds.):*Proc. CLA 2011*, 2011, pp. 413–416.

Sco, GS Formal Concept Analysis on Graphics Hardware.

In: Bessis N., Xhafa F. (Eds.):*Next Generation Data Technologies for Collective Computational Intelligence*,*Studies in Computational Intelligence***352**, 2011, pp. 139–165.

GS Visualising Computational Intelligence through Converting Data into Formal Concepts.

In: Andrews S., Polovina S., Hill R., Akhgar B. (Eds.):*Conceptual Structures for Discovering Knowledge, 19th International Conference on Conceptual Structures, ICCS 2011*,*Lecture Notes in Computer Science***6828**, 2011, pp. 50–62.

GS In-Close2, a High Performance Formal Concept Miner.

In:*SCIS & ISIS 2010*, 2010.

GS A Method of Transformation Between Tree and Lattice Structure for File Management.

In: Kryszkiewicz M., Obiedkov S. (Eds.):*Proc. CLA 2010*, 2010, pp. 104–115.

GS Analysis of Large Data Sets using Formal Concept Lattices. *ACM Transactions on Information and System Security (TISSEC)***13**(4)(2010).

GS Mining Roles with Multiple Objectives.

In:*Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on*, 2009, pp. 352–355.

GS A Research on Fuzzy Formal Concept Analysis Based Collaborative Filtering Recommendation System.

In:*Conceptual Structures Tools Interoperability Workshop at the 17th International Conference on Conceptual Structures*, 2009.

GS Data conversion and interoperability for FCA.

In: Adams N. M., Robardet C., Siebes A., Boulicaut J.-F. (Eds.):*Advances in Intelligent Data Analysis VIII, 8th International Symposium on Intelligent Data Analysis, IDA 2009*,*Lecture Notes in Computer Science***5772**, 2009, pp. 333–344.

GS Distributed Algorithm for Computing Formal Concepts Using Map-Reduce Framework.

In:*International Conference on Conceptual Structures (ICCS)*, 2009.

GS In-Close, a fast algorithm for computing formal concepts.

, 2 pp.

GS Interaktive Exploration von Softwaremustern. *Информационные технологии***12**(), pp. 8–13.

GS Интегральная OLAP-модель предметной области для аналитической поддержки принятия решений.

In: Kryszkiewicz M., Obiedkov S. (Eds.):*CLA 2010: Proceedings of the 7th International Conference on Concept Lattices and Their Applications*, 2010, pp. 325–337.

Sco, GS Advances in algorithms based on CbO.

In: Yager R. R., Sgurev V. S., Jotsov V. S. (Eds.):*Proceedings of IEEE CIS 2008: The Fourth International IEEE Conference on Intelligent Systems*, 2008, pp. 1535–1541, Varna, Bulgaria, 9/2008.

[IEEE, New York, USA, Drawing lattices with a geometric heuristic. DOI 10.1109/IS.2008.4670536, ISBN 978–1–4244–1740–7]

DB: WoS (WOS:000263194700112), Sco, GS, RIVPDF | abstract | 2 citations (2 GS)**Abstract**Lattices play an important role in many areas of computer science andapplied mathematics. The information, extracted from data in dataanalysis or operated with in intelligent systems, is usuallyrepresented by hierarchical structures, where relationships aredescribed by lattices. To visualize the information, one needs tovisualize lattices. We mention the existing methods of automateddrawing of lattices and focus on the geometric method introduced byWille et al. Diagrams drawn by the geometric method achieve a goodlevel of readability and aesthetic criteria while satisfying commonconventions and constraints, even for larger lattices. We discussseveral questions regarding the method and show the diagram drawingsproduced by two software programs developed at Dept. Computer Science,Palacky University, Czech Republic.**Citations**

In: Gervasi O., Murgante B., Misra S., Rocha A. M. A. C., Torre C. M., Taniar D., Apduhan B. O., Stankova E., Wang S. (Eds.):*Proc. Computational Science and Its Applications, 16th International Conference (ICCSA 2016)*,*Lecture Notes in Computer Science***9790**, 2016, pp. 480–496.

GS Using Formal Concepts Analysis Techniques in Mining Data from Criminal Databases and Profiling Events Based on Factors to Understand Criminal Environments. *Bachelor Thesis*, 2015, 40 pp.

GS Demonstration of lattice drawing algorithms (in Czech).

In: Trappl R. (Ed.):*Cybernetics and Systems 2008: Proceedings of the 19th European Meeting on Cybernetics and Systems Research*, 2008, pp. 9–14, Vienna, Austria, 3/2008.

[Austrian Society for Cybernetics Studies, Vienna, Austria, ISBN 978–3–85206–175–7]

DB: Inducing decision trees via concept lattices. RIVabstract**Abstract**The paper presents a new machine learning method of decision tree induction based on formal concept analysis (FCA). FCA is a data mining technique the output of which is a hierarchical structure of clusters extracted from data describing objects by attributes. The decision tree is derivedusing the structure of clusters (called concept lattice). The idea behind is tolook at a concept lattice as a collection of overlapping trees. The main purpose of the paper is to explore the possibility of using FCA in theproblem of decision tree induction. We present our method and providecomparisons with selected methods of decision tree induction and machine learning on testing datasets.

In: Ben Yahia S., Mephu Nguifo E., Belohlavek R. (Eds.):*Concept Lattices and their Applications*,*Lecture Notes in Artificial Intelligence***4923**, 2008, pp. 68–79, Hammamet, Tunisia, 10–11/2006.

[Springer-Verlag, Berlin Heidelberg, Germany, Direct factorization by similarity of fuzzy concept lattices by factorization of input data. DOI 10.1007/978-3-540-78921-5_4, ISBN 978–3–540–78920–8, ISSN 0302-9743 (paper), 1611–3349 (online)]

DB: WoS (WOS:000254857000004), Sco, GS, RIVPDF | abstract | 4 citations (1 WoS, 2 Sco, 4 GS)**Abstract**The paper presents additional results on factorization by similarity of fuzzyconcept lattices. A fuzzy concept lattice is a hierarchically ordered collection of clusters extracted from tabular data. The basic idea of factorization by similarity is to have, instead of a possibly large original fuzzy concept lattice, its factor lattice. The factor lattice contains less clusters than the original concept lattice but, at the same time, represents a reasonable approximation of the original concept lattice and provides us with a granular view on the original concept lattice. The factor lattice results by factorization of the original fuzzy concept lattice by a similarity relation. The similarity relation is specified by a user by means of a single parameter, called a similarity threshold. Smaller similarity thresholds lead to smaller factor lattices, i.e. to morecomprehensible but less accurate approximations of the original concept lattice. Therefore, factorization by similarity provides a trade-off between comprehensibility and precision. We first recall the notion of factorization. Second, we present a way to compute the factor lattice of a fuzzy concept lattice directly from input data, i.e. without the need to compute the possibly large original concept lattice.**Citations***International Journal of Approximate Reasoning***73**(2016), pp. 27–55.

Sco, GS Block relations in formal fuzzy concept analysis. *Dissertation Thesis*, 2012, 86 pp.

GS Closure and Interior Structures in Relational Data Analysis and Their Morphisms.

In: Kahraman C., Kerre E. E., Bozbura F. T. (Eds.):*Uncertainty Modeling in Knowledge Engineering and Decision Making*,*World Scientific Proceedings Series on Computer Engineering and Information Science***7**, 2012, pp. 652–657.

WoS, GS On Fuzzy Rough Concept Lattices.

In: Napoli A., Vychodil V. (Eds.):*Proc. CLA 2011*, 2011, pp. 115–130.

Sco, GS Block Relations in Fuzzy Setting.

In: Diatta J., Eklund P., Liquière M. (Eds.):*Proc. CLA 2007*, 2007, pp. 274–285, Montpellier, France, 10/2007.

[LIRMM & University of Montpellier II, Montpellier, France, Inducing decision trees via concept lattices. CEUR WS, Vol. 331]

DB: ScoPDF | abstract | 1 citation ()**Abstract**The paper presents a new method of decision tree induction based on formalconcept analysis (FCA). The decision tree is derived using a concept lattice,i.e.a hierarchy of clusters provided by FCA. The idea behind is to lookat a concept lattice as a collection of overlapping trees.The main purpose of the paper is to explore the possibility of using FCA in theproblem of decision tree induction. We present our method and providecomparisonswith selected methods of decision tree induction on testing datasets.**Citations***Dissertation Thesis*, 2013 Vers une approche hybride mêlant arbre de classification et treillis de Galois pour de l'indexation d'images.

In: Torra V., Narukawa Y., Yoshida Y. (Eds.):*Modeling Decisions for Artificial Intelligence: 4th International Conference*,*Lecture Notes in Artificial Intelligence***4617**, 2007, pp. 174–184, Kitakyushu, Japan, 8/2007.

[Springer-Verlag, Berlin Heidelberg, Germany, Trees in concept lattices. DOI 10.1007/978-3-540-73729-2_17, ISBN 978–3–540–73728–5, ISSN 0302-9743]

DB: WoS (WOS:000249325800017), Sco, GS, RIVPDF | abstract | 2 citations (2 WoS, 1 Sco, 2 GS) + 1 selfcitation (1 WoS, 1 Sco, 1 GS)**Abstract**The paper presents theorems characterizing concept lattices which happen to be trees after removing the bottom element. Concept lattices are the clustering/classification systems provided as an output of formal concept analysis. In general, a concept latticemay contain overlapping clusters and need not be a tree. On the other hand, tree-like classification schemes are appealing and are produced by several classification methods as the output. This paper attempts to help establish a bridge between concept lattices and tree-based classification methods. We present results presenting conditions for input data which are sufficient and necessary for the output concept lattice to form a tree after one removes its bottom element. In addition, we present illustrative examples and several remarks on related efforts and future research topics.**Citations***Discrete Applied Mathematics***190**(2015), pp. 13–23.

WoS, GS On the Galois Lattice of Bipartite Distance Hereditary Graphs. *The Scientific World Journal*(2014).

WoS, Sco, GS Improving Predictions of Multiple Binary Models in ILP. *Int. Journal of General Systems***38**(4)(2009), pp. 455–467.

WoS, Sco, GS Inducing decision trees via concept lattices.

In: Torra V., Narukawa Y., Yoshida Y. (Eds.):*Modeling Decisions for Artificial Intelligence: 4th International Conference*,*Lecture Notes in Artificial Intelligence***4617**, 2007, pp. 156–167, Kitakyushu, Japan, 8/2007.

[Springer-Verlag, Berlin Heidelberg, Germany, Lindig's algorithm for concept lattices over graded attributes. DOI 10.1007/978-3-540-73729-2_15, ISBN 978–3–540–73728–5, ISSN 0302-9743]

DB: WoS (WOS:000249325800015), Sco, GS, RIVPDF | abstract | 14 citations (9 WoS, 8 Sco, 13 GS) − 1 co-citation (1 WoS, 1 GS)**Abstract**Formal concept analysis (FCA) is a method of exploratory data analysis. The data is in the form of a table describing relationship between objects (rows) and attributes (columns), where table entries are grades representing degrees to which objects have attributes. The main output of FCA is a hierarchical structure (so-called concept lattice) of conceptual clusters (so-called formal concepts) present in the data. This paper focuses on algorithmic aspects of FCA of data with graded attributes. Namely, we focus on the problem of generating efficiently all clusters present in the data together with their subconcept-superconcept hierarchy. We present theoretical foundations, the algorithm, analysis of its efficiency, and comparison with other algorithms.**Citations**

In: Hassanien A. E., Shaalan K., Azar A. T., Gaber T.,Tolba M. F. (Eds.):*2nd International Conference on Advanced Intelligent Systems and Informatics, AISI 2016*, 2017, pp. 781–792.

GS Enhanced algorithms for fuzzy formal concepts analysis. *Soft Computing***20**(4)(2016), pp. 1485–1502.

WoS, Sco, GS Knowledge representation using interval-valued fuzzy formal concept lattice. *Applied Intelligence***40**(1)(2014), pp. 154–177.

WoS, Sco, GS Formal and relational concept analysis for fuzzy-based automatic semantic annotation. *Information Sciences***253**(2013), pp. 100–109.

WoS, Sco, GS Multi-adjoint relation equations: Definition, properties and solutions using concept lattices. *Information Sciences***225**(2013), pp. 47–54.

WoS, Sco, GS Dual multi-adjoint concept lattices. *Information Sciences***222**(2013), pp. 405–412.

WoS, Sco, GS Solving systems of fuzzy relation equations by fuzzy property-oriented concepts.

In: IEEE (Ed.):*2013 IEEE Symposium on Intelligent Agent (IA)*, 2013, pp. 36–43.

WoS Hybrid methodologies to foster Ontology-based Knowledge Management Platform.

In: Pasi G., Montero J., Ciucci D. (Eds.):*Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)*,*Advances in Intelligent Systems Research***32**, 2013, pp. 340–346.

WoS, Sco, GS Building multi-adjoint concept lattices.

In: Greco S., Bouchon-Meunier B., Coletti G., Fedrizzi M., Matarazzo B., Yager R. R. (Eds.):*Advances in Computational Intelligence, 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Proceedings, Part II*,*Communications in Computer and Information Science***298**, 2012, pp. 395–404.

GS Solving General Fuzzy Relation Equations Using Property-Oriented Concept Lattices. *Fuzzy Sets and Systems***208**(2012), pp. 95–110.

WoS, Sco, GS On multi-adjoint concept lattices based on heterogeneous conjunctors.

In: Napoli A., Vychodil V. (Eds.):*Proc. CLA 2011*, 2011, pp. 75–86.

Sco, GS Concept lattices in fuzzy relation equations.

In:*Proceedings of the 11th UK Workshop on Computational Intelligence*, 2011, pp. 61–67.

GS Fuzzy formal concept analysis and algorithm.

In: IEEE (Ed.):*2008 4th International IEEE Conference Intelligent Systems, Vols 1 and 2*, 2008, pp. 628–633.

WoS, GS Optimal decompositions of matrices with grades. *Annals of Fuzzy Mathematics and Informatics*(2015).

GS Rules for Computing Fixpoints of a Fuzzy Closure Operator.

In: Ben Yahia S., Mephu Nguifo E. (Eds.):*Proc. 4th Int. Conf. on Concept Lattices and Their Applications, CLA 2006*, 2006, pp. 57–69, Hammamet, Tunisia, 10–11/2006.

[Faculté des Sciences de Tunis, Université Centrale, Tunis, Tunisia, ISBN 978–9973–61–481–0]

DB: On factorization by similarity of fuzzy concept lattices with hedges. GS, RIVPDF | abstract | 1 selfcitation (1 GS)**Abstract**The paper presents results on factorization by similarity of fuzzy concept lattices with hedges. Factorization of fuzzy concept lattices including a fast way to compute the factor lattice was presented in our earlier papers. The basic idea is to have, instead of a whole fuzzy concept lattice, its factor lattice. The factor lattice results by factorizingthe original fuzzy concept lattice by a similarity relation which is specified by a user by a single parameter (similarity threshold). The main purpose is to have a smaller lattice which can be seen as a reasonable approximation of the original, possibly large, fuzzy concept lattice. In this paper, we extend the existing results to the case of fuzzy concept lattices with hedges, i.e. with parameters controlling the size of a fuzzy concept lattice.**Citations***Dissertation Thesis*, 2006, 77 pp.

GS Factorizing Fuzzy Concept Lattices by Similarity.

In: Schärfe H., Hitzler P., Øhrstrøm P. (Eds.):*Proc. 14th International Conference on Conceptual Structures, ICCS 2006*,*Lecture Notes in Artificial Intelligence***4068**, 2006, pp. 117–130, Aalborg, Denmark, 7/2006.

[Springer-Verlag, Berlin Heidelberg, Germany, Thresholds and shifted attributes in formal concept analysis of data with fuzzy attributes. DOI 10.1007/11787181_9, ISBN 3–540–35893–5, ISSN 0302-9743]

DB: WoS (WOS:000239625500008), Sco, GS, RIVPDF | abstract | 16 citations (9 WoS, 9 Sco, 16 GS) − 2 co-citations (1 WoS, 1 Sco, 2 GS) + 3 selfcitations (1 WoS, 1 Sco, 3 GS)**Abstract**We focus on two approaches to formal concept analysis (FCA) of data with fuzzy attributes recently proposed in the literature, namely, on the approach via hedges and the approach via thresholds. Both of the approaches present parameterized ways to FCA of data with fuzzy attributes. Our paper shows basic relationships between the two of the approaches. Furthermore, we show that the approaches can be combined in a natural way, i.e. we present an approach in which one deals with both thresholds and hedges. We argue that while the approach via thresholds is intuitively appealing, it can be considered a special case of the approach via hedges. An important role in this analysis is played by so-called shifts of fuzzy attributes which appeared earlier in the study of factorization of fuzzy concept lattices. In addition to fuzzy concept lattices, we consider the idea of thresholds for the treatment of attribute implications from tables with fuzzy attributes and prove basic results concerning validity and non-redundant bases.**Citations**

In: IEEE (Ed.):*2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)*,*IEEE International Conference on Fuzzy Systems*, 2015.

Sco, GS Cuts or thresholds, what is the best reduction method in fuzzy formal concept analysis?. *Journal of Computer and System Sciences***81**(1)(2015), pp. 208–218.

WoS, GS Decision implication canonical basis: a logical perspective. *Knowledge-based Systems***89**(2015), pp. 192–202.

WoS, Sco, GS On the use of irreducible elements for reducing multi-adjoint concept lattices. *International Journal of Computer Mathematics***92**(9)(2015), pp. 1855–1873.

WoS, GS On the use of thresholds in multi-adjoint concept lattices. *International Journal of Machine Learning and Cybernetics***5**(4)(2014), pp. 509–516.

GS Decision implications: a logical point of view. *International Journal of General Systems***43**(2)(2014), pp. 105–134.

WoS, Sco, GS Fuzzy and rough formal concept analysis: a survey. *Fundamenta Informaticae***111**(1)(2011), pp. 33–45.

WoS, Sco, GS The Construction of Fuzzy Concept Lattices Based on (theta, sigma)-Fuzzy Rough Approximation Operators. *Dissertation Thesis*, 2010, 297 pp.

GS Essays on using formal concept analysis in information engineering. *Knowledge-Based Systems***23**(6)(2010), pp. 497–503.

WoS, Sco, GS Attribute reduction in fuzzy concept lattices based on the T implication. *Annals of Mathematics and Artificial Intelligence***59**(2)(2010), pp. 187–200.

WoS, Sco, GS Factorization of fuzzy concept lattices with hedges by modification of input data. *Logic Journal of the IGPL***17**(2)(2009), pp. 205–223.

WoS, Sco, GS Factorization of residuated lattices.

In: Wen P., Li Y,, Polkowski L., Yao Y., Tsumoto S., Wang G. (Eds.):*Rough Sets and Knowledge Technology, Proceedings*,*Lecture Notes in Artificial Intelligence***5589**, 2009, pp. 601–609.

WoS, GS Fuzzy Concept Lattices Determined by (theta, sigma)-Fuzzy Rough Approximation Operators.

In: Belohlavek R., Kuznetsov S. O. (Eds.):*Proc. CLA 2008*, 2008, pp. 231–241.

Sco, GS Factorization of Concept Lattices with Hedges by Means of Factorization of Residuated Lattices.

In: Galindo J. (Ed.):*Handbook of Research on Fuzzy Information Processing in Databases*, 2008, pp. 462–489.

GS Relational Data, Formal Concept Analysis, and Graded Attributes. *Information Sciences***177**(15)(2007), pp. 3186–3191.

WoS, Sco, GS A note on variable threshold concept lattices: Threshold-based operators are reducible to classical concept-forming operators.

GS A Theoretical Framework for Distributed Reduction in Concept Lattice. *Int. Journal of Foundations of Computer Science***19**(2)(2008), pp. 255–269.

WoS, Sco, GS Fast factorization by similarity of fuzzy concept lattices with hedges. *Dissertation Thesis*, 2006, 77 pp.

GS Factorizing Fuzzy Concept Lattices by Similarity.

In: Ben Yahia S., Mephu Nguifo E. (Eds.):*Proc. 4th Int. Conf. on Concept Lattices and Their Applications, CLA 2006*, 2006, pp. 57–69.

GS On factorization by similarity of fuzzy concept lattices with hedges.

In: Zajac M. (Ed.):*International Conference in Applied mathematics for undergraduate and graduate students, ISCAM 2005*, 2005, pp. 41–45, Bratislava, Slovak Rep., 4/2005.

[Slovak University of Technology] Similarity clarification in formal concept analysis.abstract | 1 selfcitation (1 GS)**Abstract**Formal concept analysis (FCA) is an algebraic method of data miningwhich aims at extracting a hierarchical structure (so-called conceptlattice) of clusters (so-called formal concepts) from object-attributedata tables. One of the hottest problems in application of FCA is alarge number of clusters extracted from data. We try to cope withthis problem by reducing the amount of input data by the well-knownmethod called clarification, extended to fuzzy setting. This reductionhas the effect of clustering of similar formal concepts and therebymakes the concept lattice smaller.**Citations***Dissertation Thesis*, 2006, 77 pp.

GS Factorizing Fuzzy Concept Lattices by Similarity.

In:*Proc. 5th Int. Conf. on Recent Advances in Soft Computing, RASC 2004*, 2004, pp. 578–583, Nottingham, UK, 12/2004.

[, ISBN 1–84233–110–8]

DB: Direct factorization in formal concept analysis by factorization of input data. GS, RIVPDF | abstract | 2 citations (2 GS) + 4 selfcitations (4 GS)**Abstract**Formal concept analysis aims at extracting a hierarchical structure(so-called concept lattice) of clusters (so-called formal concepts)from object-attribute data tables. We present an algorithm forcomputing a factor lattice of a concept lattice from the data and auser-specified similarity threshold a. The presented algorithmcomputes the factor lattice directly from the data, without firstcomputing the whole concept lattice and then computing the collectionsof clusters. We present theoretical insight and examples.**Citations***International Journal of General Systems***43**(2)(2014), pp. 105–134.

WoS, GS Fuzzy and rough formal concept analysis: a survey. *Dissertation Thesis*, 2010, 297 pp.

GS Essays on using formal concept analysis in information engineering. *Int. Journal of Foundations of Computer Science***19**(2)(2008), pp. 255–269.

WoS, Sco, GS Fast factorization by similarity of fuzzy concept lattices with hedges. *Dissertation Thesis*, 2006, 77 pp.

GS Factorizing Fuzzy Concept Lattices by Similarity.

In: Ben Yahia S., Mephu Nguifo E. (Eds.):*Proc. 4th Int. Conf. on Concept Lattices and Their Applications, CLA 2006*, 2006, pp. 57–69.

GS On factorization by similarity of fuzzy concept lattices with hedges.

In: Schärfe H., Hitzler P., Øhrstrøm P. (Eds.):*Proc. 14th International Conference on Conceptual Structures, ICCS 2006*,*Lecture Notes in Artificial Intelligence***4068**, 2006, pp. 117–130.

WoS, Sco, GS Thresholds and shifted attributes in formal concept analysis of data with fuzzy attributes.

In:*AISTA 2004 in Cooperation with the IEEE Computer Society Proceedings*, 2004, pp. ?–?, Kirchberg – Luxembourg, Luxembourg, 11/2004.

[University of Canberra, Canberra, Australia, ISBN 2–9599776–8–8]

DB: Fast factorization by similarity in formal concept analysis. GSPDF | abstract**Abstract**Formal concept analysis aims at extracting a hierarchical structure(so-called concept lattice) of clusters (so-called formal concepts)from object-attribute data tables. An important problem inapplications of formal concept analysis is a possibly large number ofclusters extracted from data. Factorization is one of the methodsbeing used to cope with the number of clusters. We present analgorithm for computing a factor lattice of a concept lattice from thedata and a user-specified similarity threshold $a$. The factor latticeis smaller than the original concept lattice and its size depends onthe similarity threshold. The elements of the factor lattice arecollections of clusters which are pairwise similar in degree at least$a$. The presented algorithm computes the factor lattice directly fromthe data, without first computing the whole concept lattice and thencomputing the collections of clusters. We present theoretical insightand examples for demonstration.

In: Snášel V., Bělohlávek R. (Eds.):*CLA 2004, Concept Lattice and their Applications, proceedings of the 2nd international workshop*, 2004, pp. 47–57, Ostrava, Czech Rep., 9/2004.

[VŠB – Technical University of Ostrava, Ostrava, Czech Rep., Fast factorization of concept lattices by similarity: solution and an open problem. CEUR WS, Vol. 110, ISBN 80–248–0597–9]

DB: Sco, GSPDF | abstract | 10 citations (10 GS) + 2 selfcitations (2 GS)**Abstract**An important problem in applications of formal concept analysis is a possibly large number of clusters extracted from data. Factorization is one of the methods being used to cope with the number of clusters. We present an algorithm for computing a factor lattice of a concept lattice from the data and a user-specified similarity threshold $a$. The elements of the factor lattice are collections of clusters which are pairwise similar in degree at least $a$. The presented algorithm computes the factor lattice directly from the data, without first computing the whole concept lattice and then computing the collections of clusters. We present theoretical insight and examples for demonstration, and an open problem.**Citations***Applied Mechanics and Materials***373-375**(2013), pp. 1714–1718.

GS Semantic Web Ontology Integration Based on Formal Concept Analysis.

In: Zheng F. (Ed.):*Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)*,*Advances in Intelligent Systems Research***41**, 2013, pp. 233–236.

GS Concept Lattice-Based Semantic Web Service Ontology Merging. *Master Thesis*, 2011, 72 pp.

GS Summarization Of Real Valued Biclusters. *Discrete Mathematics***311**(18-19)(2011), pp. 2049–2063.

GS Power contexts and their concept lattices. *Annals of Mathematics and Artificial Intelligence***61**(3)(2011), pp. 245–256.

GS Similarity measures in formal concept analysis.

In: Wen D., Wang R., Zhou J. (Eds.):*Communication Software and Networks, 2010. ICCSN '10. Second International Conference on*, 2010, pp. 439–443.

GS Concept Lattice-Based Semantic Web Service Matchmaking.

2009, 17 pp.

GS Concept Similarity in Fuzzy Formal Concept Analysis for Semantic Web. *Knowledge-Based Systems***21**(1)(2008), pp. 80–87.

GS Concept similarity in Formal Concept Analysis: An information content approach. *Information Sciences***176**(18)(2006), pp. 2624–2641.

GS Ontology-based concept similarity in Formal Concept Analysis.

GS Towards a Theory of Power Concept Lattices. *Int. Journal of Foundations of Computer Science***19**(2)(2008), pp. 255–269.

WoS, Sco, GS Fast factorization by similarity of fuzzy concept lattices with hedges.

In: Ben Yahia S., Mephu Nguifo E. (Eds.):*Proc. 4th Int. Conf. on Concept Lattices and Their Applications, CLA 2006*, 2006, pp. 57–69.

GS On factorization by similarity of fuzzy concept lattices with hedges.

## Other publications

*Habilitation Thesis*, 2015, 92 pp.

[Olomouc, Czech Rep.] Computing and Applying Formal Concepts: Algorithms and Methods.PDF | abstract**Abstract**The thesis summarizes and further comments on selected results of research in the algorithms and applications of FCA conducted by the author at the Department of Computer Science, Palacký University Olomouc, during the years 2007–2012, with remarks to further results from years 2013–2014. In the first years, the research was focused on the topic of applying FCA in classification of data with the aim at developing a decision tree induction method based on FCA. Then, due to a growing need that appeared in several related problem areas, the focus moved to the development of efficient algorithms for computing formal concepts–the basic units of data studied in FCA–which could be effectively used in applications of FCA, most eminently in data mining. In the last years and present the focus of the research has been on applying FCA for preprocessing of data for other data mining and machine learning methods. The aim was to use Boolean matrix factorization, performed via the structures of FCA, as a method for solving the feature extraction problem. The thesis is composed of a compact introduction to FCA and a collection of papers with commented summaries of results. The collection consists of 4 impacted journal papers and 6 peer-reviewed papers published in proceedings of international conferences. The contribution of the author of this thesis in all of the papers is at least proportional to the number of (co-)authors, in 4 papers more than proportional and of 3 papers he is the only author. The summaries, for the sake of consistency and self-containedness and also to better show the relationships between the topics, contain also (shortened) descriptions of the algorithms and methods developed in the respective papers. This includes also pseudocodes, illustrative examples, and sample results from experimental evaluations.*Dissertation Thesis*, 2006, 77 pp.

[Palacký University Olomouc, Olomouc, Czech Rep.]

DB: Factorizing Fuzzy Concept Lattices by Similarity. GSPDF | abstract**Abstract**The goal of the work presented in the thesis was development of new, or improvement of existing, algebraic methods of data mining (DM), namely clustering techniques. Data mining is an area of data analysis the subject of which is to unfold, reveal or discover (blankly "dig out'' or "mine'') the relatively smaller amount of unknown, essential information or knowledge and relationships hidden in (usually) large amount of data. Clustering techniques as methods of DM does so by grouping (somehow) similar records and forming so-called clusters together with relationships between them.The work focuses on relatively new method of data mining called Formal Concept Analysis (FCA) and utilizes the (algebraic) clustering technique of factorization to reduce the amount of structured information on the output of FCA. Formal concept analysis is an algebraic method of data mining which aims at extracting a hierarchical st ructure (so-called concept lattice) of clusters (so-called formal concepts) from data tables describing the relationship between objects and attributes. In basic setting, the attributes are binary presence/absence attributes, but the work deals with graded (fuzzy) attributes,like big or cheap, which apply to objects to intermediate degrees, not necessarily false or true only. For dealing with data tables containing fuzzy attributes, various extensions of FCA have been proposed, e.g. Burusco & Fuentes-Gonzales, Pollandt, Bělohlávek et al., Krajči, BenYahia et al.One of the hottest problems in application of FCA is a possibly large number of clusters/concepts extracted from data. There have been several methods proposed to help to reduce or manage the sizeof the structure. Interesting two parametrized approaches, via hedges and via thresholds, were recently proposed in the literature. In the work, we show basic relationships between the two approaches. An important role in this analysis is played by so-called shifts of fuzzy attributes.Factorization, is one of the methods trying to apply some sort of clustering to make the structure of formal concepts smaller. Instead of viewing the whole, possibly large, concept lattice, it provides a granular (approximation) view through a smaller factor lattice the elements of which are collections ofpairwise similar original concepts. In order to compute the factor lattice (directly by definition), we have to compute the whole lattice and then compute all the collections of similar concepts.The work focuses on a parametrized method of factorization for data with fuzzy attributes. The similarity relation is induced by a parameter specified by a user and computed from input data. Then, the size of factor lattice depends on the parameter. The most important part of the work is the presentation of an easy and fast way to compute the (parametrized) factor concept lattice directly from input data. We also explore the use of factorization in fuzzy concept lattices with hedges. The interesting aspect is discussed: the question of relationships between adjusting input data, modifying the formation of concepts and factorization of the structure of original concepts, since each of these approaches leads to the same result - factor lattice. Beside the theoretical insight, we present the extensive experiments on small and middle-sized data tables from different areas of human activity (demography, sociology).*Diploma Thesis*, 2003, 74 pp.

[Palacký University Olomouc, Olomouc, Czech Rep.] Algorithms for drawing ordered sets and lattices (in Czech).PDF | abstract**Abstract**Goal of the work was to explore, implement and compare various methods of authomatic generation of (Hasse) diagram of ordered sets, in particular lattices. Input is enumeration of elements of an ordered set and the order relation only, or manually created preliminary version of the diagram, output then as readable as possible diagram which at the same time satisfies certain conventions and requirements. The algorithms of giagram generation include both known and less known methods of diagram and graph generation, and also one own method. Generated diagram can be further manually edited and improved. Eventually one can export it to METAPOST or Encapsulated PostScript, native format for storing the set and diagram is XML. The tool was written primarily for unix-like systems but it is ported to Windows systems too. The tool is fully internationalized, at present English and Czech translations exist.*LinuxEXPRES, (Czech) Linux magazine*, 2005–2007, cca 50 columns (12 columns, 10 doublecolumns, 6 pages) pp.

[QCM, Brno, Czech Rep., ISSN 1214–8733 (paper), 1801–3996 (online)] Kernel, Linux kernel news (in Czech).