This study extends the web classification approach through a proximity-based fuzzy clustering sensible to the influence of the page. The proximity-based fuzzy clustering works in an unsupervised manner, augmented by a certain auxiliary supervision mechanism. The supervision scheme is realized via a number of proximity “hints” (constraints) that specify an extent to which some pairs of patterns are regarded similar or different. The hints are provided externally to the clustering algorithm and improve the searching activity by customizing the user’s navigation. In this paper we focus on the feature spaces corresponding to the Web data characterizing the context analysis and we discuss how the knowledge extraction process identifies the right context.
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