A hybrid algorithm for web document clustering based on frequent term sets and k-means

Abstract

In order to conquer the major challenges of current web document clustering, i.e. huge volume of documents, high dimensional process and understandability of the cluster, we propose a simple hybrid algorithm (SHDC) based on top-k frequent term sets and k-means. Top-k frequent term sets are used to produce k initial means, which are regarded as initial clusters and further refined by k-means. The final optimal clustering is returned by k-means while the understandable description of clustering is provided by k frequent term sets. Experimental results on two public datasets indicate that SHDC outperforms other two representative clustering algorithms (the farthest first k-means and random initial k-means) both on efficiency and effectiveness. © Springer-Verlag Berlin Heidelberg 2007.