GAKC: A new GA-based k clustering algorithm

摘要：

Clustering is an important, hard and active topic in data analysis and pattern recognition. K clustering is a branch of data clustering where the number of clusters is know in advance. Recently, spectral clustering (SC) becomes one of the most popular and appealing k clustering methods because of its generality, efficiency and its rich theoretical foundation. But the final results obtained from SCs depend on spectral relaxation which may have no guarantee on the quality of the solution. In order to overcome the SCs shortcoming, we propose an effective GAKC algorithm by using a genetic algorithM to search for the optimal cluster result of SCs. The algorithm uses group number coding chromosome, a new uniform crossover operator and exponential mutation rate. To verify the effectiveness of GAKC, a comparison among the experimental results of the proposed GAKC, a classical GA-based method by Ujjwal Malulik and the SC methods by SM and NJW on a reallife data set is presented. The conclusion comes that the proposed algorithm can gain much more accurate clustering result.

关键词： pattern recongnition Data clustering genetic algorithm spectral clustering

作者: Li Xiaohong Luo Min

作者单位: School of Computer, Wuhan University, Wuhan, 430072, Hu bei,P.R.China School of Computer, Wuhan University, Wuhan,430072, Hu bei, P.R.China The Key Laboratory of Aerospac

会议类型: 国际会议

会议名称: Second International Symposium on Information Science and Engineering(第二届信息科学与工程国际会议)

会议地点: 上海

会议语种:英文

页码: 334-338

在线出版日期: 2009-12-26（万方平台首次上网日期，不代表论文的发表时间）