Title page for etd-0907112-210106


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URN etd-0907112-210106
Author Wen-Ling Chen
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2011
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title A Novel Multiobjective EA-based Clustering Algorithm with Automatic Determination of the Number of Clusters
Date of Defense 2012-07-27
Page Count 49
Keyword
  • diversity
  • multiobjective clustering
  • Clustering
  • Abstract 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.
    Advisory Committee
  • Chung-Nan Lee - chair
  • Tzung-Pei Hong - co-chair
  • Chun-Wei Tsai - co-chair
  • Ming-Chao Chiang - advisor
  • Files
  • etd-0907112-210106.pdf
  • Indicate in-campus at 99 year and off-campus access at 99 year.
    Date of Submission 2012-09-07

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