Fuzzy clustering-based formal concept analysis for association rules mining

Applied Artificial Intelligence

Volumn 26, Issue 3, 2012, Pages 274-301

  Kumar, Ch Aswani ^a  

^a VIT UNIVERSITY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION RULES;

DATA SET; FORMAL CONCEPTS ANALYSIS; FORMAL CONTEXTS; FUNCTIONAL DEPENDENCY; FUZZY K MEANS CLUSTERING; PERFORMANCE; REAL-WORLD; REDUCED DATA;

FORMAL CONCEPT ANALYSIS;

EID: 84858011023     PISSN: 08839514     EISSN: 10876545     Source Type: Journal    
DOI: 10.1080/08839514.2012.648457     Document Type: Article

References (38)

1. Agarwal, R., T. Imielinski., and, A. Swami. 1993. Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD conference on management of data, 207-213. New York, NY: ACM Press.
2. Aswani Kumar, Ch. 2009. Analysis of unsupervised dimensionality reduction techniques. Computer Science and Information Systems 6 (2): 8-16.
3. Aswani Kumar, Ch. 2011a. Reducing data dimensionality using random projections and fuzzy k-means clustering. International Journal of Intelligent Computing and Cybernetics 4 (3): 353-365.
4. Aswani Kumar, Ch. 2011b. Knowledge discovery in data using formal concept analysis and random projections. International Journal of Applied Mathematics and Computer Science 21 (4): 745-756.
5. Aswani Kumar, Ch., and, S. Srinivas. 2010a. Concept lattice reduction using fuzzy k means clustering. Expert Systems with Applications 37 (3): 2692-2704.
6. Aswani Kumar, Ch., and, S. Srinivas. 2010b. Mining association rules in healthcare data using formal concept analysis and singular value decomposition. Journal of Biological Systems 18 (4): 787-807.
7. Aswani Kumar, Ch., and, S. Srinivas. 2010c. A note on weighted fuzzy k means clustering for concept decomposition. Cybernetics and Systems 41 (6): 455-467.
8. Belohlavek, R., and, V. Vychodil. 2009. Formal concept analysis with background knowledge: Attribute priorities. IEEE Transactions on Systems, Man and Cybernetics 39: 399-409.
9. Belohlavek, R., D. Grissa., S. Guillaume., E. M. Nguifo., and, J. Outrata. 2011. Boolean factors as a means of clustering of interestingness measures of association rules. In Proceedings of the international conference on concept lattices and their applications, ed. A. Napoli and V. Vychodil, 207-222.
10. Ceglar, A., and, J. Roddick. 2006. Association mining. ACM Computing Surveys 38: 5-es.
11. Cheung, K. S. K., and, D. Vogel. 2005. Complexity reduction in lattice based information retrieval. Information Retrieval 8: 285-299. (Pubitemid 40526285)
12. Cole, R., P. Eklund., and, G. Stumme. 2003. Document retrieval for email search and discovery using formal concept analysis. Applied Artificial Intelligence 17 (3): 257-280.
13. Dhillon, I. S., and, D. Modha. 2001. Concept decomposition for large sparse text data using clustering. Machine Learning 42: 143-175. (Pubitemid 32872402)
14. Dobsa, J., and, B. D. Basic. 2004. Comparison of information retrieval techniques: Latent semantic indexing and concept indexing. Journal of Information and Organizational Sciences 28: 1-17.
15. Doring, C., M. J. Lessot., and, R. Kruse. 2006. Data analysis with fuzzy clustering methods. Computational Statistics and Data Analysis 51: 192-214. (Pubitemid 44634807)
16. Du, Y., and, H. M. Li. 2010. Strategy for mining association rules for web pages based on formal concept analysis. Applied Soft Computing 10: 772-783.
17. Duequenne, V. 1999. Latticial structures in data analysis. Theoretical Computer Science 217: 407-436.
18. Formica, A. 2006. Ontology based concept similarity in formal concept analysis. Information Sciences 176: 2624-2641. (Pubitemid 43946423)
19. Horner, V. 2007. Developing a consumer health informatics decision support system using formal concept analysis. Masters' thesis, University of Pretoria.
20. Kuznetsov, S., C. Obiedkov., and, C. Roth. 2007. Reducing the representation complexity of lattice based taxonomies. In: Proceedings of 15th international conference on conceptual structures, 241-254. Berlin: Springer. (Pubitemid 350271726)
21. Liu, M., M. Shao., W. Zhang., and, W. C. Wu. 2007. Reduction method for concept lattices based on rough set theory and its application. Computers and Mathematics with Applications 53: 1390-1410. (Pubitemid 46756160)
22. Maddouri, M. 2005. A formal concept analysis approach to discover association rules from data. In: Proceedings of international conference on concept lattices and their applications, 10-21. Czech Republic.
23. Missaoui, R., L. Nourine., and, Y. Renaud. 2008. Generating positive and negative exact rules using formal concept analysis. In Proceedings of international conference on formal concept analysis, ed. R. Medina and S. Obiedkov, 169-181. Berlin: Springer-Verlag.
24. Pasquier, N. 2000. Mining association rules using formal concept analysis. In Working with conceptual structures, contributions to ICCS, ed. G. Stumme., 259-264. Verlag Shaker Aachen.
25. Pattison, P. E., and, R. L. Brieger. 2002. Lattices and dimensional representations: Matrix decompositions and ordering structures. Social Networks 24: 423-444.
26. Priss, U. 2007. Formal concept analysis in information science. Annual Review of Information Science and Technology 40: 521-543.
27. Snasel, V., H. M. D. Abdulla., and, M. Polovincak. 2008. Behavior of the concept lattice reduction to visualizing data after using matrix decompositions. In Proceedings of 4th international conference on innovations in information technology, 392-396.
28. Song, S.-J., E.-H. Kim., H.-G. Kim., and, H. Kumar. 2011. Query based association rule mining supporting user perspective. Computing 93: 1-25.
29. Stumme, G. 2002. Efficient data mining based on formal concept analysis. In Proceedings of 13th international conference on database and expert systems applications, 534-546. Berlin: Springer-Verlag.
30. Stumme, G. 2009. Formal concept analysis. In Handbook on Ontologies, ed. S. Staab. and R. Studer., 177-199. Berlin: Springer-Verlag.
31. Stumme, G., R. Taouil., Y. Bastide., N. Pasquier., and, L. Lakhal. 2001. Intelligent structuring and reducing of association rules with formal concept analysis. In Proceedings of joint German/Austrian conference on artificial intelligence, 335-350. (Pubitemid 33347346)
32. Touzi, A. G., A. Thabet., and, M. Sassi. 2011. Efficient reduction of the number of association rules using fuzzy clustering on the data. In Advances in swarm intelligence, ed. Y. Tan et al., Proceedings of 2nd ICSI- Part II, 191-199. Berlin: Springer-Verlag.
33. Valtchev, R., R. Missaoui., and, R. Godin. 2004. Formal concept analysis for knowledge discovery and data mining: The new challenges. In Proceedings of 2nd international conference on formal concept analysis, 352-371. Berlin: Springer-Verlag. (Pubitemid 38271487)
34. Venter, F. J., G. D. Oosthuizen., and, J. D. Ross. 1997. Knowledge discovery in databases using concept lattices. Experts Systems with Applications 13: 259-264. (Pubitemid 127401091)
35. Wang, L., and, X. Liu. 2008. Concept analysis via rough set and AFS algebra. Information Sciences 178 (21): 4125-4137.
36. Wille, R. 2008. Formal concept analysis as applied lattice theory. In Concept Lattices and Their Applications, ed. et al. 42-67. Springer-Verlag. (Pubitemid 351467635)
37. Wu, W. Z., Y. Leung., and, J. S. Mi. 2009. Granular computing and knowledge reduction in formal contexts. IEEE Transactions on Knowledge and Data Engineering 21: 1461-1474.
38. Zaki, M. J., and, M. Ogihara. 1998. Theoretical foundations of associations rules. In Proceedings of 3rd ACM SIGMOD workshop on research issues in data mining and knowledge discovery. New York, NY: ACM Press.