Fuzzy association rule mining framework and its application to effective fuzzy associative classification

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volumn 1, Issue 6, 2011, Pages 477-495

  Kianmehr, Keivan ^a   Kaya, Mehmet ^b   ElSheikh, Abdallah M ^c   Jida, Jamal ^d   Alhajj, Reda ^c,e  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

^b FIRAT UNIVERSITY   (Turkey)

^c UNIVERSITY OF CALGARY   (Canada)

^d LEBANESE UNIVERSITY   (Lebanon)

^e GLOBAL UNIVERSITY   (Lebanon)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION RULES; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; DATA MINING; FORECASTING; FUZZY RULES; GENE EXPRESSION; GENETIC ALGORITHMS; MACHINE LEARNING; MEMBERSHIP FUNCTIONS; MULTIOBJECTIVE OPTIMIZATION; VECTORS;

ASSOCIATIVE CLASSIFICATION; BENCHMARK DATASETS; CLASSIFICATION FRAMEWORK; FUZZY ASSOCIATION RULE; GENE EXPRESSION CLASSIFICATION; INTEGRATED FRAMEWORKS; MACHINE LEARNING TECHNIQUES; MULTI-OBJECTIVE GENETIC ALGORITHM;

LEARNING ALGORITHMS;

EID: 84873117418     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.40     Document Type: Article

References (43)

1. Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB '94). San Francisco, CA: Morgan Kaufmann Publishers Inc.; 1994, 487-499.
2. Zadeh LA. Fuzzy sets. Inf Control 1965, 8:338-353.
3. Jang JSR, Sun CT. Neuro-fuzzy modeling and control. Proc IEEE 1995, 83:378-406.
4. Srikant R, Agrawal R. Mining quantitative association rules in large relational tables. SIGMOD Rec 1996, 25:1-12.
5. Miller RJ, Yang Y. Association rules over interval data. SIGMOD Rec 1997, 26:452-461.
6. Lent B, Swami AN, Widom J. Clustering association rules. In: ICDE '97: Proceedings of the Thirteenth International Conference on Data Engineering. Washington, DC: IEEE Computer Society; 1997, 265-269.
7. Yager RR. Fuzzy summaries in database mining. In: CAIA '95: Proceedings of the 11th Conference on Artificial Intelligence for Applications. Washington, DC: IEEE Computer Society; 1995, 265.
8. Hirota K, PedryczW. Linguistic data mining and fuzzy modelling. In: Proceedings of IEEE International Conference on Fuzzy Systems. Vol. 2. Washington, DC: IEEE Computer Society; 1996, 1448-1496.
9. Pedrycz W. Fuzzy sets technology in knowledge discovery. Fuzzy Sets Syst 1998, 98:279-290.
10. Chan KCC, Au W-H. Mining fuzzy association rules. In: CIKM '97: Proceedings of the Sixth International Conference on Information and Knowledge Management. New York: ACM; 1997, 209-215.
11. Au W, Chan K. An effective algorithm for discovering fuzzy rules in relational databases. In: Proceedings of IEEE International Conference on Fuzzy Systems. Washington, DC: IEEE Computer Society; 1998, 1314-1319.
12. Hong T-P, Kuo C-S, Chi S-C. A fuzzy data mining algorithm for quantitative values. In: KES. Adelaide: IEEE Press; 1999, 480-483.
13. Hong T-P, Kuo C-S, Chi S-C. Mining association rules from quantitative data. Intell Data Anal 1999, 3:363-376.
14. Ishibuchi H, Nakashima T, Yamamoto T. Fuzzy association rules for handling continuous attributes. In: Proceedings of IEEE ISIE. Pusan: IEEE Press; 2001, 118-121.
15. Gyenesei A. A fuzzy approach for mining quantitative association rules. Turku Center for Computer Science, TUCS Technical Reports No. 336, May 2000.
16. Gyenesei A. Mining weighted association rules for fuzzy quantitative items. Turku Center for Computer Science, TUCS Technical Reports No. 346, May 2000.
17. Zhang W. Mining fuzzy quantitative association rules. In: ICTAI '99: Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence. Washington, DC: IEEE Computer Society; 1999, 99-102.
18. Kuok CM, Fu A, Wong MH. Mining fuzzy association rules in databases. SIGMOD Rec 1998, 27:41-46.
19. Ng RT, Han J. Efficient and effective clustering methods for spatial data mining. In: VLDB. Santiago de Chile: Morgan Kaufmann Publishers Inc.; 1994, 144-155.
20. Kaya M, Alhajj R, Polat F, Arslan A. Efficient automated mining of fuzzy association rules. In: DEXA '02: Proceedings of the 13th International Conference on Database and Expert Systems Applications. London: Springer-Verlag; 2002, 133-142.
21. Chien BC, Lin ZL, Hong TP. An efficient clustering algorithm for mining fuzzy quantitative association rules. In: Proceedings of IFSA World Congress and NAFIPS International Conference. Vol. 3. Vancouver, BC: IEEE Press; 2001, 1306-1311.
22. Kaya M, Alhajj R. Facilitating fuzzy association rules mining by using multi-objective genetic algorithms for automated clustering. In: ICDM '03: Proceedings of the Third IEEE International Conference on Data Mining. Washington, DC: IEEE Computer Society; 2003, 561.
23. Clark P, Matwin S. Using qualitative models to guide inductive learning. In: Proceedings of International Conference on Machine Learning (ICML '93). Amherst, MA: Morgan Kaufmann Publishers Inc.; 1993, 49-56.
24. Pazzani MJ, Mani S, ShankleWR. Beyond concise and colorful: learning intelligible rules. In: Proceedings of the Third International Conference on KnowledgeDiscovery and Data Mining (KDD '97). Menlo Park, CA: AAAI Press; 1997, 235-238.
25. Liu B, Hsu W, Ma Y. Integrating classification and association rule mining. In: Proceedings of the Fourth International Conference on KnowledgeDiscovery and Data Mining (KDD '98). New York: AAAI Press; 1998, 80-86.
26. Li W, Han J, Pei J. Cmar: accurate and efficient classification based on multiple class-association rules. In: Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM '01). San Jose, CA: IEEE Press; 2001, 369-376.
27. Quinlan JR. C4.5: Programs for Machine Learning. San Francisco, CA: Morgan Kaufmann Publishers Inc.; 1993.
28. Yin X, Han J. Cpar: classification based on predictive association rules. In: SIAM International Conference on Data Mining (SDM '03). San Francisco, CA; 2003, 331-335.
29. Coenen F, Leng P. The effect of threshold values on association rule based classification accuracy. Data Knowl Eng 2007, 60:345-360.
30. Chen Z, Chen G. Building an associative classifier based on fuzzy association rules. Int J Comput Intell Syst 2008, 1:262-273.
31. Ishibuchi H, Nojima Y. Analysis of interpretabilityaccuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. Int J Approx Reason 2007, 44:4-31.
32. Alonso JM, Magdalena L, González-Rodríguez G. Looking for a good fuzzy system interpretability index: an experimental approach. Int J Approx Reason 2009, 51:115-134.
33. Gacto M, Alcala R, Herrera F. Integration of an index to preserve the semantic interpretability in the multiobjective evolutionary rule selection and tuning of linguistic fuzzy systems. IEEE Trans Fuzzy Syst 2010, 18:515-531.
34. Gonzalez A, Perez R. Slave: a genetic learning system based on an iterative approach. IEEE Trans Fuzzy Syst 1999, 7:176-191.
35. Gonzalez A, Perez R. Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Trans SystMan Cybern B 2001, 31:417-425.
36. Kaya M, Alhajj R. Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining. J Intell Inf Syst 2008, 31:243-264.
37. Kianmehr K, Özyer T, Kaya M, Alhajj R. Wrapping vrxquery with self-adaptive fuzzy capabilities. Web Intell Agent Syst 2009, 7:269-279.
38. Cong G, Tan K-L, Tung AKH, Xu X. Mining topk covering rule groups for gene expression data. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data (SIGMOD '05). New York: ACM; 2005, 670-681.
39. Ishibuchi H, Nakashima T. Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Trans Ind Electron 1999, 46:157-168.
40. Coenen F, Leng P. The effect of threshold values on association rule based classification accuracy. Data Knowl Eng 2007, 60:345-360.
41. Frank A, Asuncion A. UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science; 2010. http://archive.ics.uci.edu/ml. Accessed December 10, 2009.
42. Lu X, Lu X, Wang Z, Iglehart J, Zhang X, Richardson A. Predicting features of breast cancer with gene expression patterns. Breast Cancer Res Treat 2008, 108:191-201.
43. Taylor KM, Morgan HE, Smart K, Zahari NM, Pumford S, Ellis IO, Robertson FRJ, Nicholson RI. The emerging role of the liv-1 subfamily of zinc transporters in breast cancer. Mol Med 2007, 13:396-406.