A modified approach to speed up genetic-fuzzy data mining with divide-and-conquer strategy

Chun Hao Chen, Tzung Pei Hong*, Vincent S. Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In the past, we proposed a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions based on the divide-and-conquer strategy. In this paper, an enhanced approach, called the cluster-based genetic-fuzzy mining algorithm, is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It first divides the chromosomes in a population into k clusters by the k-means clustering approach and evaluates each individual according to its own information and the information of the cluster it belongs to. The final best sets of membership functions in all the populations are then gathered together for mining fuzzy association rules. Experimental results also show the effectiveness and efficiency of the proposed approach.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages1-6
Number of pages6
DOIs
StatePublished - 1 Dec 2007
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 25 Sep 200728 Sep 2007

Publication series

Name2007 IEEE Congress on Evolutionary Computation, CEC 2007

Conference

Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007
CountrySingapore
Period25/09/0728/09/07

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