A multi-objective genetic-fuzzy mining algorithm

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

*Corresponding author for this work

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

11 Scopus citations

Abstract

In this paper, we propose a multi-objective genetic-fuzzy mining algorithm for extracting both membership functions and association rules from quantitative transactions. Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions. It consists of two factors, coverage factor and overlap factor, to avoid two bad types of membership functions. The second one is the total number of large 1-itemsets from a given set of minimum support values. The two criteria have a trade-off relationship. Experimental results also show the effectiveness of the proposed approach in finding the Pareto-front membership functions.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Granular Computing, GRC 2008
Pages115-120
Number of pages6
DOIs
StatePublished - 30 Dec 2008
Event2008 IEEE International Conference on Granular Computing, GRC 2008 - Hangzhou, China
Duration: 26 Aug 200828 Aug 2008

Publication series

Name2008 IEEE International Conference on Granular Computing, GRC 2008

Conference

Conference2008 IEEE International Conference on Granular Computing, GRC 2008
CountryChina
CityHangzhou
Period26/08/0828/08/08

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