A new algorithm for mining fuzzy association rules

Proceedings of 2004 International Conference on Machine Learning and Cybernetics

Volumn 3, Issue , 2004, Pages 1635-1640

  Gao, Ya ^a   Ma, Jun ^a   Lin, Ma ^b  

^a SOUTHWEST JIAOTONG UNIVERSITY   (China)

^b North China Pharmaceutical Corporation   (China)

Author keywords

Association rule; Data mining; Fuzzy implication operator

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; FUZZY SETS; INFORMATION RETRIEVAL; KNOWLEDGE BASED SYSTEMS;

FUZZY IMPLICATION OPERATORS; MINING ALGORITHMS; REDUNDANT FUZZY ASSOCIATION (RFA) RULES;

DATA MINING;

EID: 6344256549     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. R. Agrawal, T. Imielinske, and A. Swami. Mining association rules between sets of items in large databases. Proc. 1993 ACM-SIGMOD Internat. Conf. Management of Data, pp. 207-206, Washington, D.C., 1993.
2. R. Agrawal, and R. Srikant. Fast algorithms for mining association rules. VLDB'94. Los Angeles: Mogan kaufmann, 1994.
3. Wai-Ho Au, and K.C.C. Chan. An effective algorithm for discovering fuzzy rules in relation databases. Proc. IEEE world congress on Computational intelligence, pp. 1314-1319, 1998.
4. W. Bandler, and L. J. Kohout, Fuzzy power sets and fuzzy implication operators. Fuzzy sets and Systems, 4(1), pp. 13-30,1980.
5. W. Bandler, and L. J. Kohout, Semantics of implication operators and fuzzy relational products. Internat. J. of Man-Machine Studies, 12(1), pp. 89-116,1980.
6. G. Chen, Q.Wei, and E.E. Kerre. Fuzzy data mining: Discovery of fuzzy generalized association rules. G. Bordogna and G. Pasi, (eds.) Recent Issues on Fuzzy Databases. Springer-Verlag, 2000.
7. G. Chen, P. Yan, and E. Kerre, Mining Fuzzy Implication-Based Association Rules in Quantitative Databases, Proceedings of FLINS2002, Belgium, 2002.
8. M. Delgado, D. Sanchez, and M.A. Vila. Acquisition of fuzzy association rules from medical data. S. Barro and R. Marin, (eds.) Fuzzy Logic in Medicine. Physica Verlag, 2000.
9. U. Fayyad, G. Piatetsky-shapiro, and P. Smyth, "From Data Mining to Knowledge Discovery: An overview", Fayyad U., Piatetsky-Shapiro G., Smyth P. and Uthurusamy R.(eds.) Advances in Knowledge Discovery and Data Mining, Cambridge, MA: AAAI Press, pp. 1-30,1996.
10. Gyenesei, A fuzzy approach for mining quantitative association rules, TUCS technical reports 336, University of Turku, Department of Computer Science, Lemminkisenkatu 14, Finland, 2000.
11. E. Hüllermeier and J. Beringer. Mining implication-based fuzzy association rules in databases, B. Bouchon-Meunier, L. Foulloy, and R.R. Yager, (eds.) Intelligent Systems for Information Processing: From Representation to Applications. Elsevier, 2003.
12. E.E. Kerre, Introduction to the Basic Principles of Fuzzy Set Theory and Some of Its Application. Communication & Cognition, Gent, Belgium. 1991.
13. G.J. Klir and B. Yuan. Fuzzy Sets and Fuzzy Logic-Theory and Applications. Prentice Hall, New Jersey, 1995.
14. C. Man Kuok, A. Fu, and M. Hon Wong. Mining fuzzy association rules in databases. SIGMOD Record, 27 pp.41-46, 1998.
15. H. Mannila, H. Toivonen, and I. Verkamo, Efficient Algorithms for Discovering Association Rules, Piatetsky-Shapiro, G. and Frawley, W. J.(eds.) Knowledge Discovery in Databases, AAAI Press/The MIT Press, Menlo Park, California, 1991.
16. D. Ruan, and E.E. Kerre, Fuzzy implication operators and generalized fuzzy method of cases, Fuzzy Sets and systems, 54(1), pp. 23-38,1993.
17. Savasere, E. Omiecinski, and S. Navathe, An Efficient Algorithm for Mining Association Rules in Large Dtabses, Proceedings of the 21st VLDB Conference, Zurich Swizerland, pp. 432-443, 1995
18. R. Srikant and R. Agrawal. "Mining Quantitative Association Rules in Large Relational Tables". Proc. of 1996 ACM-SIGMOD Internat. Conference management of Data. pp. 1-12, Montreal, Canada, 1996.
19. http://www.epsilon.com/
20. http://www.ncc.up.pt/