Title page for etd-0907112-211759


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URN etd-0907112-211759
Author Huei-jyun Song
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 Hyper-Heuristic Clustering Algorithm
Date of Defense 2012-07-26
Page Count 54
Keyword
  • diversified detection
  • metaheuristics algorithm
  • clustering problem
  • Hyper-heuristic algorithm
  • Abstract The so-called heuristics have been widely used in solving combinatorial optimization problems because they provide a simple but effective way to find an approximate solution. These technologies are very useful for users who do not need the exact solution but who care very much about the response time. For every existing heuristic algorithm has its pros and cons, a hyper-heuristic clustering algorithm based on the diversity detection and improvement detection operators to determine when to switch from one heuristic algorithm to another is presented to improve the clustering result in this paper. Several well-known datasets are employed to evaluate the performance of the proposed algorithm. Simulation results show that the proposed algorithm can provide a better clustering result than the state-of-the-art heuristic algorithms compared in this paper, namely, k-means, simulated annealing, tabu search, and
    genetic k-means algorithm.
    Advisory Committee
  • Chu-sing Yang - chair
  • Chun-wei Tsai - co-chair
  • Shiann-rong Kuang - co-chair
  • Ming-chao Chiang - advisor
  • Files
  • etd-0907112-211759.pdf
  • Indicate in-campus at 99 year and off-campus access at 99 year.
    Date of Submission 2012-09-07

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