Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association Rules Mining

I
ICDM
2004
Fourth IEEE International Conference on Data Mining (ICDM'04)
Abstract - Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association Rules Mining

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	Similar Articles Articles by Mehmet Kaya Articles by Reda Alhajj

Fourth IEEE International Conference on Data Mining (ICDM'04)

Brighton, United Kingdom

November 01-November 04

ISBN: 0-7695-2142-8

	ASCII Text	x
	Mehmet Kaya, Reda Alhajj, "Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association Rules Mining," Data Mining, IEEE International Conference on, pp. 431-434, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10050, author = {Mehmet Kaya and Reda Alhajj}, title = {Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association Rules Mining}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {431-434}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10050}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association Rules Mining SN - 0-7695-2142-8 SP431 EP434 A1 - Mehmet Kaya, A1 - Reda Alhajj, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Mehmet Kaya, Firat University, Turkey

Reda Alhajj, University of Calgary, Canada

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10050

In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We compare the proposed multi-objective GA based approach with: 1) CURE based approach; 2) Chien et al clustering approach. Experimental results on 100K transactions extracted from the adult data of United States census in year 2000 show that the proposed method exhibits good performance over the other two approaches in terms of runtime, number of large itemsets and number of association rules.

Citation:

Mehmet Kaya, Reda Alhajj, "Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association Rules Mining," icdm, pp.431-434, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004

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