Effect of similar behaving attributes in mining of fuzzy association rules in the large databases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3980 LNCS, Issue , 2006, Pages 1100-1109

  Farzanyar, Zahra ^a   Kangavari, Mohammadreza ^a   Hashemi, Sattar ^a  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

Author keywords

Data mining; Fuzzy association rules; Linguistic terms

Indexed keywords

DATA MINING; FUZZY SETS; LINGUISTICS; USER INTERFACES;

FUZZY ASSOCIATION RULES; LINGUISTIC TERMS;

DATABASE SYSTEMS;

EID: 33745955634     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11751540_120     Document Type: Conference Paper

References (33)

1. G. Piatetsky-Shapiro, and W.J. Frawley (Eds.), Knowledge Discovery in Databases, AAAI/MIT Press, (1991).
2. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, (1996).
3. J.W. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco, (2001).
4. R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD International Conference on Management of Data, May (1993), pp. 207-216.
5. R. Srikant, R. Agrawal, Mining Quantitative Association Rules in Large Relational Tables, Proc. of ACM-SIGMOD, Montreal, Canada, (1996).
6. L.A. Zadeh, Fuzzy sets, Inform, and Control 8 (1965) 338-353.
7. E. Hullermeier 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).
8. 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).
9. R.J. Miller, Y. Yang, Association rules over interval data, in: Proc. ACM SIGMOD Internat. Conf. Management of Data, (1997), pp. 452-461.
10. K.C.C. Chan and W.-H. Au, "An Effective Algorithm for Mining Interesting Quantitative Association Rules," in Proc. of the 12th ACM Symp. on Applied Computing, San Jose, CA, Feb. (1997), pp. 88-90.
11. K.C.C. Chan and W.-H. Au, "Mining Fuzzy Association Rules," in Proc. of the 6th ACM Int'l Conf. on Information and Knowledge Management, Las Vegas, Nevada, Nov. (1997), pp. 209-215.
12. T.P. Hong, C.S. Kuo, S.C. Chi, Mining association rules from quantitative data, Intell. Data Anal. 3 (1999) 363-376.
13. Chen, G. Q.; Wei, Q., (2002). Fuzzy Association Rules and the Extended Mining Algorithms, Information Sciences, 147, pp. 201-228.
14. C.H. Cai, W.C. Fu, C.H. Cheng, W.W. Kwong, Mining association rules with weighted items, in: Proc. IDEAS, (1998), pp. 68-77.
15. A. Gyenesei, Mining weighted association rules for fuzzy quantitative items, TUCS Technical Report No: 346, May (2000).
16. S.Yue, E. Tsang, D.Yeung, D. Shi, Mining fuzzy association rules with weighted items, Proc. IEEE Internat. Conf. Systems Man Cybernet. (2000) 1906-1911.
17. K. M. Lee Mining generalized Fuzzy quantitative Association Rules with Fuzzy Generalization Hierarchies, 0-7803-7078-3/01/$10.00 (C)(2001) IEEE.
18. Y.C. Hu, R.Sh. Chen, G.H. Tzeng Discovering fuzzy association rules using fuzzy partition methods, Knowledge-Based Systems 16 (2003) Elsevier, 137-147
19. W.H. Au and K. C. C. Chan Mining Fuzzy Association Rules in a Bank-Account Database, IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 11, NO. 2, APRIL (2003)
20. Y. Gao, J. Ma, L. Ma, A new algorithm for mining fuzzy association rules, Proc. IEEE Internat. Conf. Machine Learning and Cybernetics.(2004).
21. H. Ishibuchi, T. Nakashima, T.Yamamoto, Fuzzy association rules for handling continuous attributes, in: Proc. IEEE ISIE, (2001),
22. A. Gyenesei, A fuzzy approach for mining quantitative association rules, TUCS Technical Report No: 336, March (2000).
23. W. Zhang, Mining fuzzy quantitative association rules, Proc. IEEE Internat. Conf. Tools Artif. Intell. (1999) 99-102.
24. R.R.Yager, Fuzzy summaries in database mining, Proc. Conf. Artif. Intell. Appl. (1995) 265-269
25. Mills, F. Statistical Methods, Pitman, (1955).
26. A.W.C. Fu, M.H.Wong, S.C. Sze,W.C.Wong,W.L.Wong,W.K.Yu, Finding fuzzy sets for the mining of association rules for numerical attributes, in: Proc. IDEL, October (1998), pp. 263-268.
27. R. Ng, J. Han, Efficient and effective clustering methods for spatial data mining, in: Proc. Internat. Conf. Very Large Databases, (1994).
28. K. Hirota, W. Pedrycz, Linguistic data mining and fuzzy modelling, Proc. IEEE Internat. Conf. Fuzzy Systems, vol. 2, (1996), pp. 1448-1496.
29. M. Kaya, R. Alhajj, Facilitating fuzzy association rules mining by using multi-objective genetic algorithms for automated clustering, in: Proc. IEEE Internat. Conf. Data Mining, November (2003).
30. M. Kaya, R. Alhajj. Genetic algorithm based framework for mining fuzzy association rules, Fuzzy Sets and Systems (2004) Elsevier.
31. L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning, Information Science (Part 1) (1975).
32. M. R. Civanlar and H. J. Trussell, "Constructing membership functions using statistical data," In Fuuy Sets und System, vol. 18, pp.1-14, (1986)
33. R. Kohonen, "Self-Oganization and Associate Memory," Springer, Berlin, (1988)