Techniques of document clustering: A review

The document clustering technique is widely recognized as a useful tool for information retrieval, organizing web documents, text mining and so on. The purpose of this paper is to review various document clustering techniques, and to discuss research issues for enhancing effectiveness or efficiency of the clustering methods. We explore extensive literature on non-hierarchical methods (single-pass methods), hierarchical methods (single-link, complete-link, etc.), dimensional reduction methods (LSI, principal component analysis, etc.), probabilistic methods, data mining techniques, and so on. In particular, this paper focuses on typical techniques, such as the k-means algorithm, the leader-follower algorithm, self-organizing map (SOM), single- or complete-link methods, bisecting k-means methods, latent semantic indexing (LSI), Gaussian-Mixture model and so on. After reviewing the techniques and algorithms, we discuss research issues on document clustering; computational complexity, feature extraction (selection of words), methods for defining term weights and similarity, and evaluation of results.