AN EFFECTIVE CLUSTERING METHOD USING A DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM-BASED HYBRID APPROACH

摘要：

The purpose of this paper is to present and evaluate an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values using EM algorithm.It is well-known that EM approach has a drawback-local optimal solution, so we propose a novel hybrid algorithm of the Discrete Particle Swarm Optimization (DPSO) and the EM approach to improve the global search performance. We evaluate this hybrid approach on 4 real-world data sets from UCI repository. In a number of experiments and comparisons,the hybrid DPSO+EM algorithm exhibits a more effective and outperforms the EM approach.

关键词： Clustering Na(i)ve Bayes Particle Swarm Optimization algorithm EM algorithm

作者: JING-HUA GUAN DA-YOU LIU HAI-YANG JIA PENG YU

作者单位: Sch.of Computer S&T, Jilin University, Postfach 13 00 12, Changchun, China Key Lab.of Symbolic Computation & Knowledge Eng.of Ministry of Education, Jilin University., Postfac

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 1114-1119

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）