Competitive learning with pairwise constraints for text

Abstract

Text clustering and constrained clustering both have been an important area of research over the years. The commonly used vector space representation of text data involves high dimensional sparse matrices. We present algorithms which are improvements over the existing algorithms namely, Online Linear Constrained Vector Quantization Error (O-LCVQE) and Constrained Rival Penalized Competitive Learning (C-RPCL). We use the concept of spherical k-means in place of Euclidean distance based traditional k-means. Several experiments demonstrate that the proposed algorithms work better for high dimensional text data in terms of normalized mutual information. We further show that k-means with rival penalized competitive learning is a much better alternative than simple k-means when applied on text data. The performances of k-means and spherical k-means come closer, when the distance function is weighted by the neuron winning frequency. � Springer International Publishing Switzerland 2014.