A multi-objective genetic-fuzzy mining algorithm

Author

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

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng-Kung Univ., Tainan

fYear

2008

fDate

26-28 Aug. 2008

Firstpage

115

Lastpage

120

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.

Keywords

Pareto optimisation; data mining; fuzzy set theory; genetic algorithms; Pareto-front membership functions; association rules; coverage factor; multiobjective genetic-fuzzy mining algorithm; overlap factor; quantitative transactions; Association rules; Biological cells; Computer science; Data mining; Evolutionary computation; Genetic algorithms; Genetic engineering; Itemsets;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing, 2008. GrC 2008. IEEE International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4244-2512-9

Electronic_ISBN

978-1-4244-2513-6

Type

conf

DOI

10.1109/GRC.2008.4664771

Filename

4664771