Granularity of attributes in formal concept analysis

Information Sciences

Volumn 260, Issue , 2014, Pages 149-170

  Belohlavek, Radim ^a   De Baets, Bernard ^b   Konecny, Jan ^a  

^a PALACKÝ UNIVERSITY   (Czech Republic)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

Algorithms for data management; Binary data; Concept lattice; Formal concept analysis; Interactive data exploration

Indexed keywords

BASIC STRUCTURE; BINARY DATA; CONCEPT LATTICES; FORMAL CONCEPTS; GRANULARITY LEVELS; INPUT DATAS; INTERACTIVE DATA EXPLORATION; ZOOM-OUT;

ALGORITHMS; EXPERIMENTS; INFORMATION ANALYSIS; INFORMATION MANAGEMENT;

FORMAL CONCEPT ANALYSIS;

EID: 84891683073     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2013.10.021     Document Type: Article

References (24)

1. R. Belohlavek Fuzzy Relational Systems: Foundations and Principles 2002 Kluwer Academic/Plenum Publishers New York
2. R. Belohlavek, and V. Vychodil Formal concept analysis with background knowledge: attribute priorities IEEE Transactions on Systems, Man, and Cybernetics, Part C 39 4 2009 399 409
3. R. Belohlavek, and V. Vychodil Discovery of optimal factors in binary data via a novel method of matrix decomposition Journal of Computer and System Sciences 76 1 2010 3 20
4. R. Belohlavek, E. Sigmund, and J. Zacpal Evaluation of IPAQ questionnaires supported by formal concept analysis Information Sciences 181 10 2011 1774 1786
5. C. Carpineto, and G. Romano Concept Data Analysis Theory and Applications 2004 J. Wiley
6. W.C. Cho, W. Richards, Improvement of precision and recall for information retrieval in a narrow domain: reuse of concepts by formal concept analysis, in: IEEE/WIC/ACM International Conference on Web Intelligence 2004 (WI04), 2004, pp. 370-376.
7. U. Dekel, Y. Gil, Visualizing class interfaces with formal concept analysis, in: ACM SIGPLAN OOPSLA03, Anaheim, California, 2003, pp. 288-289.
8. A. Frank, A. Asuncion, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml], University of California, School of Information and Computer Science, Irvine, CA, 2010.
9. V. Ganapathy, D. King, T. Jaeger, S. Jha, Mining security-sensitive operations in legacy code using concept analysis, in: Proc. 29th Int. Conference on Software Engineering (ICSE 2007), 2007, pp. 458-467.
10. B. Ganter Attribute exploration with background knowledge Theoretical Computer Science 217 1999 215 233
11. B. Ganter, and R. Wille Formal Concept Analysis Mathematical Foundations 1999 Springer-Verlag Berlin
12. M. Kaytoue, S.O. Kuznetsov, A. Napoli, and S. Duplessis Mining gene expression data with pattern structures in formal concept analysis Information Sciences 181 10 2011 1989 2001
13. W. Kneale, and M. Kneale The Development of Logic 1984 Oxford University Press
14. J. Konecny Isotone fuzzy Galois connections with hedges Information Sciences 181 10 2011 1804 1817
15. S. Kuznetsov, and S. Obiedkov Comparing performance of algorithms for generating concept lattices Journal of Experimental and Theoretical Artificial Intelligence 14 2-3 2002 189 216
16. D. Maier The Theory of Relational Databases 1983 Computer Science Press Rockville
17. Y. Okubo, M. Haraguchi, Finding conceptual document clusters with improved top-n formal concept search, in: IEEE/WIC/ACM International Conference on Web Intelligence 2006 (WI'06), 2006, pp. 347-351.
18. J. Poelmans, P. Elzinga, D.I. Ignatov, and S.O. Kuznetsov Semi-automated knowledge discovery: identifying and profiling human trafficking International Journal of General Systems 41 8 2012 774 804
19. G. Snelting, and F. Tip Understanding class hierarchies using concept analysis ACM Transactions on Programming Languages and Systems 22 2000 540 582
20. M. Streckenbach, G. Snelting, Refactoring class hierarchies with KABA, in: ACM SIGPLAN OOPSLA 2004, 2004, pp. 315-330.
21. P. Tonella Using a concept lattice of decomposition slices for program understanding and impact analysis IEEE Transactions on Software Engineering 29 2003 495 509
22. R. Wille, Restructuring lattice theory: an approach based on hierarchies of concepts, in: I. Rival, Ordered Sets, Reidel, Dordrecht, Boston, 1982, pp. 445-470.
23. L.A. Zadeh Toward human level machine intelligence - is it achievable? The need for a paradigm shift IEEE Computational Intelligence Magazine 3 3 2008 11 22
24. M.J. Zaki Mining non-redundant association rules Data Mining and Knowledge Discovery 9 2004 223 248