| URN |
etd-0907112-211759 |
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| Author |
Huei-jyun Song |
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| Author's Email Address |
No Public. |
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| Statistics |
This thesis had been viewed 5357 times. Download 0 times. |
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| Department |
Computer Science and Engineering |
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| Year |
2011 |
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| Semester |
2 |
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| Degree |
Master |
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| Type of Document |
|
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| Language |
zh-TW.Big5 Chinese |
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| Title |
A Hyper-Heuristic Clustering Algorithm |
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| Date of Defense |
2012-07-26 |
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| Page Count |
54 |
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| Keyword |
diversified detection
metaheuristics algorithm
clustering problem
Hyper-heuristic algorithm
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| 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. |
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| Advisory Committee |
Chu-sing Yang - chair
Chun-wei Tsai - co-chair
Shiann-rong Kuang - co-chair
Ming-chao Chiang - advisor
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| Files |
Indicate in-campus at 99 year and off-campus access at 99 year. |
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| Date of Submission |
2012-09-07 |
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