A divide-and-conquer genetic-fuzzy mining approach for items with multiple minimum supports

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

*Corresponding author for this work

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

1 Scopus citations

Abstract

Since items may have their own characteristics, different minimum support values and membership functions may be specified for different items. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is designed for finding minimum support values, membership functions, and fuzzy association rules. Possible solutions are evaluated by their requirement satisfaction divided by their suitability of derived membership functions. The proposed GA framework maintains multiple populations, each for one item's minimum support value and membership functions. The final best minimum support values and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experimental results also show the effectiveness of the proposed approach.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
Pages1231-1235
Number of pages5
DOIs
StatePublished - 7 Nov 2008
Event2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 - Hong Kong, China
Duration: 1 Jun 20086 Jun 2008

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
CountryChina
CityHong Kong
Period1/06/086/06/08

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