Genetic clustering algorithms

Yu-Chiun Chiou, Lawrence W. Lan*

*此作品的通信作者

研究成果: Article同行評審

73 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)413-427
頁數15
期刊European Journal of Operational Research
135
發行號2
DOIs
出版狀態Published - 1 十二月 2001

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