Title :
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
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;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1714253
