Extracting membership functions in fuzzy data mining by Ant Colony Systems

Author

Hong, Tzung-Pei ; Tung, Ya-Fang ; Wang, Shyue-Liang ; Wu, Min-Thai ; Wu, Yu-Lung

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung

Volume

7

fYear

2008

fDate

12-15 July 2008

Firstpage

3979

Lastpage

3984

Abstract

Ant colony systems (ACS) have been successfully applied to optimization problems in recent years. However, few works have been done on applying ACS to data mining. This paper proposes an ACS-based algorithm to extract membership functions in fuzzy data mining. The membership functions are first encoded into binary bits and then fed into the ACS to search for the optimal set of membership functions. An example is given to demonstrate the proposed algorithm. Numerical experiments are also made to show the performance of the proposed approach.

Keywords

data mining; fuzzy set theory; optimisation; ant colony system; fuzzy data mining; membership function; Association rules; Computer science; Cybernetics; Data engineering; Data mining; Fuzzy sets; Fuzzy systems; Information management; Machine learning; Machine learning algorithms; Ant colony system; Association rule; Data mining; Fuzzy set; Membership function;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4621098

Filename

4621098