K-means-based fuzzy classifier design

Author

Wong, Ching-Chang ; Chen, Chia-Chong ; Yeh, Shih-Liang

Author_Institution

Dept. of Electr. Eng., Tamkang Univ., Tamsui, Taiwan

Volume

1

fYear

2000

fDate

7-10 May 2000

Firstpage

48

Abstract

In this paper, a method based on the K-means algorithm is proposed to efficiently design a fuzzy classifier so that the training patterns can be correctly classified by the proposed approach. In this method, the K-means algorithm is first used to partition the training data for each class into several clusters, and the cluster center and the radius for each cluster are calculated. Then, a fuzzy system design method that uses a fuzzy rule to represent a cluster is proposed such that a fuzzy classifier can be efficiently constructed to correctly classify the training data. The proposed method has the following features: 1) it does not need prior parameter definition; 2) it only needs a short training time; and 3) it is simple. Finally, two examples are used to illustrate and examine the proposed method for the fuzzy classifier design

Keywords

fuzzy set theory; fuzzy systems; learning (artificial intelligence); pattern classification; K-means algorithm; fuzzy classifier; fuzzy rule; fuzzy system; learning patterns; pattern classification; Algorithm design and analysis; Clustering algorithms; Design methodology; Fuzzy systems; Genetic algorithms; Handwriting recognition; Image recognition; Partitioning algorithms; Pattern classification; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on

Conference_Location

San Antonio, TX

ISSN

1098-7584

Print_ISBN

0-7803-5877-5

Type

conf

DOI

10.1109/FUZZY.2000.838632

Filename

838632