A new efficient text clustering ensemble algorithm based on semantic sequences

Abstract

The idea of cluster ensemble is combining the multiple clustering of a data set into a consensus clustering for improving the quality and robustness of results. In this paper, a new text clustering ensemble (TCE) algorithm is proposed. First, text clustering results of applying k-means and semantic sequence algorithms are produced. Then in order to generate co-association matrix between semantic sequences, the clustering results are combined based on the overlap coefficient similarity concept. Finally, the ultimate clusters are obtained by merging documents corresponding to similar semantic sequence on this matrix. Experiment results of proposed method on real data sets are compared with other clustering results produced by individual clustering algorithms. It is showed that TCE is efficient especially on long documents set. © 2013 Springer-Verlag Berlin Heidelberg.