Hypermetric k-means clustering for content-based document management

Abstract

Text-mining methods have become a key feature for homeland-security technologies, as they can help explore effectively increasing masses of digital documents in the search for relevant information. This research presents a model for document clustering that arranges unstructured documents into content-based homogeneous groups. The overall paradigm is hybrid because it combines pattern-recognition grouping algorithms with semantic-driven processing. First, a semantic-based metric measures distances between documents, by combining a content-based with a behavioral analysis; the metric considers both lexical properties and the structure and styles that characterize the processed documents. Secondly, the model relies on a Radial Basis Function (RBF) kernel-based mapping for clustering. As a result, the major novelty aspect of the proposed approach is to exploit the implicit mapping of RBF kernel functions to tackle the crucial task of normalizing similarities while embedding semantic information in the whole mechanism. © 2009 Springer-Verlag Berlin Heidelberg.