A New Feature Selection Method for Text Clustering

摘要

Feature selection methods have been successfully applied to text categorization but seldom applied to text clusteringdue to the unavailability of class label information. In this paper, anew feature selection method for text clustering based on expectation maximization and cluster validity is proposed. It uses supervised feature selection method on the intermediate clustering resultwhich is generated during iterative clustering to do feature selection for text clustering; meanwhile, the Davies-Bouldin's index isused to evaluate the intermediate feature subsets indirectly. Thenfeature subsets are selected according to the curve of the DaviesBouldin's index. Experiment is carried out on several populardatasets and the results show the advantages of the proposedmethod.