Clustering problem using adaptive genetic algorithm

Authors:
Qingzhan Chen;Jianghong Han;Yungang Lai;Wenxiu He;Keji Mao
Affiliations:
Zhejiang University of Technology, Hangzhou, Zhejiang, China;Heifei University of Technology, Hefei, Anhui, China;Zhejiang University of Technology, Hangzhou, Zhejiang, China;Zhejiang University of Technology, Hangzhou, Zhejiang, China;Zhejiang University of Technology, Hangzhou, Zhejiang, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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Abstract

Clustering is very important to data analysis and data minig. The K-Means algorithm, one of the partitional clustering approaches, is an iterative clustering technique that has been applied to many practical clustering problems successfully. However, the K-Means algorithm suffers from several drawbacks. In this paper, an adaptive genetic algorithm be present , it solve disadvantages of K-Means by combine parallel genetic algorithm, evolving flow and adaptive. Experimental results show that the adaptive genetic algorithm have advantages over traditional Clustering algorithm.