Clustering hypertext document collection is an important task in Information Retrieval. Most clustering methods are based on document content and do not take into account the hyper-text links. Here we propose a novel PageRank based clustering (PRC) algorithm which uses the hypertext structure. The PRC algorithm produces graph partitioning with high modularity and coverage. The comparison of the PRC algorithm with two content based clustering algorithms shows that there is a good match between PRC clustering and content based clustering.

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The ability to perform an exploratory search and retrieval of relevant documents from a large collection of domain-specific documents is an important requirement both in the field of medicine and other areas. In this paper, we present a unsupervised distributional clustering technique called SOPHIA. SOPHIA provides a semantically meaningful visual clustering of the document corpus in conjunction with an intuitive interactive search facility. We assess the effectiveness of SOPHIA's cluster-based information retrieval for the MEDLINE testset collection known as OHSUMED.

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