Differential Evolution for Multi-Objective Clustering

摘要：

This work describes a Differential Evolution (DE) for multi-objective clustering. Compared to traditional clustering algorithms, we evaluate two objectives--the compactness in clusters and the looseness between different clusters. DE is a population based search algorithm, which requires few control variables and it is robust, easy to implement. To optimize the two measures simultaneously, a weighing factor method is used for the clustering problem. Experiments on several data sets from UCI machine learning repository show that this new clustering method can achieve good results.

关键词： differential evolution compactness looseness

作者: Hui Wang Sanyou Zeng Liang Chen Hui Shi Cheng Zhang

作者单位: School of Computer, China University of Geosciences, Wuhan, 430074 China;Research Center of Science School of Computer, China University of Geosciences, Wuhan, 430074 China

会议类型: 国际会议

会议名称: The Second International Symposium on Intelligence Computation and Applications(ISICA 2007)(第二届智能计算及其应用国际会议)

会议地点: 武汉

会议语种:英文

在线出版日期: 2007-09-21（万方平台首次上网日期，不代表论文的发表时间）