Abstract:

Cluster ensemble is a hot topic in data mining research.It can find a combined clustering with better quality from multiple partitions.Most of resent researches pay little attention to the qualities of cluster members.However,bad cluster members and noise may affect the ensemble result.This paper presents a clustering ensemble algorithm based on weighted co-occurrence matrix.First the co-occurrence property value matrix of the cluster members is calculated.The significance of each cluster member is evaluated through information measures of clustering evaluation.Then weighted co-occurrence matrix is generated and the final ensemble result is obtained.Experimental results show the effectiveness of the algorithms,and the clustering accuracy is improved.

Key words: cluster ensemble;co-occurrence matrix;weighted