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A Hyper-Heuristic Clustering Algorithm

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.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0907112-211759
Date07 September 2012
CreatorsSong, Huei-jyun
ContributorsShiann-rong Kuang, Chun-wei Tsai, Ming-chao Chiang, Chu-sing Yang
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0907112-211759
Rightsuser_define, Copyright information available at source archive

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