DocumentCode :
3097918
Title :
An ACO-based Approach to Improve C-means Clustering Algorithm
Author :
Huang, Wenliang ; Gou, Jin ; Wu, Huifeng
Author_Institution :
Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou
fYear :
2006
fDate :
Nov. 28 2006-Dec. 1 2006
Firstpage :
12
Lastpage :
12
Abstract :
This paper presents an improved C-means clustering algorithm based on ACO. The proposed method use pheromone to evaluate individual colony´s iterative result. In contrast with the existing C-means clustering algorithm, method in the paper need not appoint the number and pre-centers of clusters beforehand and it updates pheromone according to the transfer process of data points among different temporary clusters so as to avoid the local optima and reduce the iterative times to find actual cluster centers. We test its convergence performance with CRM data sets from China Unicom Corp. The experimental results show feasibility of design rationale.
Keywords :
data analysis; iterative methods; optimisation; pattern clustering; ant colony optimisation; data analysis; improved c-means clustering algorithm; iterative method; Ant colony optimization; Cities and towns; Clustering algorithms; Computational intelligence; Computational modeling; Educational institutions; Iterative algorithms; Iterative methods; Partitioning algorithms; Software algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Modelling, Control and Automation, 2006 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7695-2731-0
Type :
conf
DOI :
10.1109/CIMCA.2006.38
Filename :
4052660
Link To Document :
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