Institute of Electrical & Electronic Engineers
Clustering similar items for web text has become increasingly important in many web and Information Retrieval (IR) applications. For several kinds of web text data, it is much easier to obtain some external information other than textual features which can be utilized to improve the performance of clustering analysis. This external information, called prior information, indicates label sign and pairwise constraints on sample points. The authors propose a unifying framework that can incorporate prior information of cluster membership for web text cluster analysis and develop a novel semi-supervised clustering model.