An Efficient Clustering Approach using Ant Colony Algorithm in Mutidimensional Search Space

摘要：

Clustering is an important data analysis technique and it widely used in many field such as data mining, machine learning and pattern recognition. Ant colony optimization clus-tering is one of the popular partition algorithm. However, in mutidimensional search space, its results is usually ordinary as the disturbing of redundant information. To address the problem, this paper presents MD-ACO clustering algorithm which improves the ant structure to implement attribute reduction. Four real data sets from UCI machine learning repository are used to evaluate MD-ACO with ACO. The results show that MDACO is more competitive.

作者: Lei Jiang Lixin Ding Yang Peng Chenhong Zhao

作者单位: State Key Lab of Software Engineering (Wuhan University),Wuhan, 430072, China Key Laboratory of Know State Key Lab of Software Engineering (Wuhan University),Wuhan, 430072, China

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 1133-1137

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）