Abstract

Document clustering is an aggregation of related documents to a cluster based on the similarity evaluation task between documents and the representatives of clusters. Terms and their discriminating features of terms are the clue to the clustering and the discriminating features are based on the term and document frequencies. Feature selection method on the basis of frequency statistics has a limitation to the enhancement of the clustering algorithm because it does not consider the contents of the cluster objects. In this paper, we adopt a content-based analytic approach to refine the similarity computation and propose a keyword-based clustering algorithm. Experimental results show that content-based keyword weighting outperforms frequency-based weighting method.

Citations