A Text Clustering Framework for Information Retrieval

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 semanticdriven processing. First, a semantic-based metric measures distances between documents, by combining content-based and behavioral analysis. Such a metric allows taking into account the lexical properties, the structure and the styles characterizing the processed documents. In a second step, the model relies on a Radial Basis Function (RBF) kernel-based mapping for clustering documents. 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. Experimental results on Reuters and Newsgroup 20 databases validate the proposed approach.