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A Novel Multiobjective EA-based Clustering Algorithm with Automatic Determination of the Number of Clusters

Automatically determining the number of clusters without a priori knowledge is a difficult research issue for data clustering problem. An effective multiobjective evolutionary algorithm based clustering algorithm is proposed to not only overcome this problem but also provide a better clustering result in this study. The proposed algorithm differs from the traditional evolutionary algorithm in the sense that instead of a single crossover operator and a single mutation operator, the proposed algorithm uses a pool of crossover operators and a pool of mutation operators that are selected at random to increase the search diversity. To evaluate the performance of the proposed algorithm, several well-known datasets are used. The simulation results show that not only can the proposed algorithm automatically determine the number of clusters, but it can also provide a better clustering result.
Date07 September 2012
CreatorsChen, Wen-Ling
ContributorsChung-Nan Lee, Ming-Chao Chiang, Tzung-Pei Hong, Chun-Wei Tsai
Source SetsNSYSU Electronic Thesis and Dissertation Archive
Detected LanguageEnglish
Rightsuser_define, Copyright information available at source archive

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