Partitioning-based clustering for Web document categorization

Clustering techniques have been used by many intelligent software agents in order to retrieve lter and categorize documents available on the World Wide Web Clustering is also useful in extracting salient features of related web documents to automatically formulate queries and search for other similar documents on the Web Traditional clustering algorithms either use a priori knowledge of document structures to de ne a distance or similarity among these documents or use probabilistic techniques such as Bayesian classi cation Many of these traditional algorithms however falter when the dimensionality of the feature space becomes high relative to the size of the document space In this paper we introduce two new clustering algorithms that can e ectively cluster documents even in the presence of a very high dimensional feature space These clustering techniques which are based on generalizations of graph partitioning do not require pre speci ed ad hoc distance functions and are capable of automatically discovering document similarities or associations We conduct several experiments on real Web data using various feature selection heuristics and compare our clustering schemes to standard distance based techniques such as hierarchical agglomeration clustering and Bayesian classi cation methods such as AutoClass