Genetic clustering algorithms |
| |
| Affiliation: | 1. Aviation and Maritime Management Department, Chang Jung Christian University, Tainan 711, Taiwan, ROC;2. Institute of Traffic and Transportation, National Chiao Tung University, 4F, 114 Sec. 1, Chung-Hsiao W. Rd., Taipei 10012, Taiwan, ROC;1. Psychiatry Research Center, Beijing Hui-Long-Guan Hospital., Peking University, Beijing, China;2. Department of Psychiatry and Behavioral Sciences, Harris County Psychiatric Center, The University of Texas Health Science Center at Houston, Houston, TX, USA;3. Department of Psychiatry and Behavioral Sciences, Center for Neurobehavioral Research on Addiction, University of Texas Health Science Center at Houston, Houston, TX, USA;1. Quantcast, San Francisco, CA, USA;2. Operations Research, North Carolina State University, Raleigh, NC, USA;3. The Department of Computer Science, North Carolina State University, Raleigh, NC 27695-8206, USA;4. King Abdulaziz University, Saudi Arabia;1. Department of Respiratory Medicine, Aichi Cancer Center Aichi Hospital, Okazaki, Japan;2. Department of Respiratory Medicine, Nagoya Medical Center, Nagoya, Japan;3. Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan;4. Department of Respiratory Medicine, Toyota Kosei Hospital, Toyota, Japan;5. Department of Respiratory Medicine, Nagoya Ekisaikai Hospital, Nagoya, Japan;6. Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan;7. Department of Respiratory Medicine, Ogaki Municipal Hospital, Ogaki, Japan;8. Division of Respiratory Medicine, Toyohashi Municipal Hospital, Toyohashi, Japan;9. Department of Biomedical Sciences, Chubu University, Kasugai, Japan |
| |
| Abstract: | This study employs genetic algorithms to solve clustering problems. Three models, SICM, STCM, CSPM, are developed according to different coding/decoding techniques. The effectiveness and efficiency of these models under varying problem sizes are analyzed in comparison to a conventional statistics clustering method (the agglomerative hierarchical clustering method). The results for small scale problems (10–50 objects) indicate that CSPM is the most effective but least efficient method, STCM is second most effective and efficient, SICM is least effective because of its long chromosome. The results for medium-to-large scale problems (50–200 objects) indicate that CSPM is still the most effective method. Furthermore, we have applied CSPM to solve an exemplified p-Median problem. The good results demonstrate that CSPM is usefully applicable. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
