关键词: 聚类集成, 特征选择, 加权融合

Abstract: To process complicated data that possesses many features, clustering ensemble based on weighted-voting is able to make a trade-off between clustering members with different qualities and improves the accuracy and stability. Towards subset selection and weight calculation, a sub-feature selection based on minimal correlation is proposed and in terms with feature-correlation, 5 different weight-calculation methods for clustering member are analyzed and compared. The experimental results show that subset generation based on minimal correlation is more effective than random sampling, and clustering ensemble based on any of the 5 weight-calculation methods gain higher accuracy than single clustering. Time-consumption among these 5 methods differ greatly.

Key words: clustering ensemble, feature selection, weighted voting