Sampling Fuzzy K-Means Clustering Algorithm Based on Clonal Optimization

Author

Yu, Haiqing ; Li, Ping ; Fan, Yugang

Author_Institution

Inst. of Ind. Process Control, Zhejiang Univ., Hangzhou

Volume

2

fYear

0

fDate

0-0 0

Firstpage

6102

Lastpage

6105

Abstract

In data mining field, FCM algorithm is an efficient method in the process of small scale low dimensional database, but the time performance of FCM algorithm can not be satisfied for the large scale high dimensional database. In this paper, a new sampling technique with clonal operation is used in SFCO algorithm to improve the time performance and the quality of clustering. The simulation experiments shows that the SFCO algorithm is an effective method in the data mining of large scale database, in addition it not only avoids the local optima and is robust to initialization, but also evidently restrains the degenerating phenomenon during the evolutionary process

Keywords

data mining; evolutionary computation; fuzzy set theory; pattern clustering; sampling methods; very large databases; SFCO algorithm; clonal optimization; data mining; dimensional database; evolutionary process; fuzzy k-means clustering; sampling technique; Clustering algorithms; Data analysis; Data mining; Databases; Industrial control; Iterative algorithms; Large-scale systems; Process control; Robustness; Sampling methods; FCM; SFCO; clonal; data mining; sampling;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714253

Filename

1714253