Text Clustering via Particle Swarm Optimization

This paper presents an approach which extends a particle swarm optimizer for variable weighting (PSOVW) to handle the problem of text clustering, called text clustering via particle swarm optimization (TCPSO). PSOVW has been exploited for evolving optimal feature weights for clusters and has demonstrated to improve the clustering quality of high-dimensional data. However, when applying it for text clustering, there exist some modifications such as the similarity measure, parameter selection and the criterion function. Our experimental results on both four structured text datasets built from 20 newsgroups as well as four large-scale text datasets selected from CLUTO show that the proposed algorithm is able to greatly improve the quality of text clustering compared to four typical clustering algorithms and one competitive subspace clustering method.