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.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0907112-210106 |
| Date | 07 September 2012 |
| Creators | Chen, Wen-Ling |
| Contributors | Chung-Nan Lee, Ming-Chao Chiang, Tzung-Pei Hong, Chun-Wei Tsai |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0907112-210106 |
| Rights | user_define, Copyright information available at source archive |
Page generated in 0.002 seconds