Document clustering by semantic smoothing and Dynamic Growing Cell Structure (DynGCS) for biomedical literature

Abstract

The general goal of clustering is to group data elements such that the intra-group similarities are high and the inter-group similarities are low. In this paper, we propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, we apply the context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. We evaluated the proposed technique on article sets from MEDLINE, the largest biomedical digital library in Biomedicine. Our experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques. © 2008 Springer-Verlag Berlin Heidelberg.