An alternative approach to discover gradual dependencies

International Journal of Uncertainty, Fuzziness and Knowldege-Based Systems

Volumn 15, Issue 5, 2007, Pages 559-570

  Berzal, F ^a   Cubero, J C ^a   Sanchez D ^a   Vila, M A ^a   Serrano, J M ^b  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF JAÉN   (Spain)

Author keywords

Approximate dependencies; Association rules; Gradual dependencies; Gradual rules

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; DATA MINING; KNOWLEDGE BASED SYSTEMS;

APPROXIMATE DEPENDENCIES; ASSOCIATION RULE MINING ALGORITHMS; GRADUAL DEPENDENCIES; GRADUAL RULES;

ASSOCIATION RULES;

EID: 35348891583     PISSN: 02184885     EISSN: None     Source Type: Journal    
DOI: 10.1142/S021848850700487X     Document Type: Conference Paper

References (18)

1. R. Agrawal, T. Imielinski, and A. Swami, Mining association rules between sets of items in large databases. In Proc. Of the 1993 ACM SIGMOD Conference, pages 207-216, 1993.
2. F. Berzal, I. Blanco, D. Sánchez, J. M. Serrano, and M. A. Vila, A definition for fuzzy approximate dependencies, Fuzzy Sets and Systems, 149(1):105-129, 2005.
3. F. Berzal, I. Blanco, D. Sánchez, and M. A. Vila, Measuring the accuracy and interest of association rules: A new framework, Intelligent Data Analysis, 6:221-235, 2002.
4. I. Blanco, M. J. Martín-Bautista, D. Sánchez, J. M. Serrano, and M. A. Vila, Using association rules to mine for strong approximate dependencies, Data Mining and Knowledge Discovery, submitted.
5. P. Bosc, L. Lietard, and O. Pivert, Functional dependencies revisited under graduality and imprecision. In Annual Meeting of NAFIPS, pages 57-62, 1997.
6. B. Bouchon-Meunier, D. Dubois, LL. Godó, and H. Prade, Fuzzy sets and possibility theory in approximate and plausible reasoning. In J.C. Bezdìk, D. Dubois, and H. Prade, editors, Fuzzy Sets in Approximate Reasoning and Information Systems, Chapter 1, pages 15-190, Kluwer Academic Publishers, 1999.
7. S. Brin, R. Motwani, J.D. Ullman, and S. Tsur, Dynamic itemset counting and implication rules for market basket data, SIGMOD Record, 26(2):255-264, 1997.
8. M. Delgado, N. Marin, D. Sánchez, and M. A. Vila, Fuzzy association rules: General model and applications, IEEE Transactions on Fuzzy Systems, 11(2):214-225, 2003.
9. D. Dubois, E. Hüllermeier, and H. Prade, A systematic approach to the assessment of fuzzy association rules, Data Mining and Knowledge Discovery, 13(2):167-192, 2006.
10. D. Dubois and H. Prade, Fuzzy functional dependencies and redundancy elimination, J. Am. Soc. Inf. Sci., 49(3):217-235, 1998.
11. M. Hahsier and K. Hornik, New probabilistic interest measures for association rules, Technical report, Department of Statistics and Mathematics, Vienna University of Economics and Business Administration, 2006.
12. Y. Huhtala, J. Karkkainen, P. Porkka, and P. Toivonen, Efficient discovery of functional and approximate dependencies using partitions. In Proc. 14th Int'l. Conf. On Data Engineering, pages 392-401, 1998.
13. E. Hüllermeier, Association rules for expressing gradual dependencies. In Proceedings PKDD 2002 Lecture Notes in Computer Science 2431, pages 200-211. 2002.
14. Chan-Man Kuok, Ada Fu, and Man Hon Wong, Mining fuzzy association rules in databases, SIGMOD Record, 27(1):41-46, 1998.
15. B. Pfahringer and S. Kramer, Compression-based evaluation of partial determinations, In Proc. First Int'l Conf. Knowledge Discovery and Data Mining (KDD'95), pages 234-239, August 1995.
16. C. Silverstein, S. Brin, and R. Motwani, Beyond market baskets: Generalizing association rules to dependence rules, Data Mining and Knowledge Discovery, 2:39-68, 1998.
17. T. Sudkamp, Examples, counterexamples, and measuring fuzzy associations, Fuzzy Sets and Systems, 149(1):57-71, 2005.
18. W. Ziarko, The discovery, analysis and representation of data dependencies in databases. In G. Piatetsky-Shapiro and W.J. Frawley, editors, Knowledge Discovery in Databases, pages 195-209. AAAI/MIT Press, 1991.