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Title page for etd-0903109-213921


URN etd-0903109-213921 Statistics This thesis had been viewed 2780 times. Download 3829 times.
Author Kai Ming Hsieh
Author's Email Address No Public.
Department Information Management
Year 2008 Semester 2
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 65
Title Combining FCM and PSO for Dynamic Fuzzy Clustering Problems
Keyword
  • Dynamic
  • Particle Swarm Optimization
  • Fuzzy c-means
  • Data Clustering
  • Data Clustering
  • Fuzzy c-means
  • Particle Swarm Optimization
  • Dynamic
  • Abstract This paper proposes a dynamic data clustering algorithm which combines the fuzzy c-means clustering method and Particle Swarm Optimization. The disadvantage of fuzzy c-means is that it requires a given number of clusters, thus this paper tries to overcome this shortcoming. The proposed approach, called fuzzy c-means with Particle Swarm Optimization (FCPSO), can automatically determine the proper number of clusters during the data clustering process. In the initial phase of FCPSO, a maximum possible cluster number is predefined. Each particle then selects its own cluster number less than the maximum number and generates cluster centers randomly. In the following iterations, a particle uses fuzzy c-means to modify its own cluster centers, to evaluate the result by using a clustering validity index, and to adjust its own cluster number according the clustering result. That is, FCPSO performs a global search iteratively to find a optional number of clusters. For each of 6 test cases, the experimental results showed that FCPSO can effectively find the best clustering configuration including the number of clusters and cluster centers.
    Advisor Committee
  • Yu-Cheng Kao - advisor
  • Shyong-Jian Shyu - co-chair
  • Yen-Ju Yang - co-chair
  • Files indicate in-campus access immediately and off-campus access at one year
    Date of Defense 2009-07-01 Date of Submission 2009-09-07


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