PROJECT TITLE :
Discovering Latent Semantics in Web Documents Using Fuzzy Clustering
Web documents are heterogeneous and advanced. There exists sophisticated associations within one internet document and linking to the others. The high interactions between terms in documents demonstrate obscure and ambiguous meanings. Economical and effective clustering methods to discover latent and coherent meanings in context are necessary. This paper presents a fuzzy linguistic topological space together with a fuzzy clustering algorithm to get the contextual that means within the net documents. The proposed algorithm extracts options from the net documents using conditional random field methods and builds a fuzzy linguistic topological area based on the associations of options. The associations of cooccurring options organize a hierarchy of connected semantic complexes referred to as “IDEAS,” whereby a fuzzy linguistic live is applied on every complex to guage one) the relevance of a document belonging to a topic, and a couple of) the difference between the other topics. Web contents are able to be clustered into topics within the hierarchy relying on their fuzzy linguistic measures; internet users will further explore the CONCEPTS of web contents accordingly. Besides the algorithm applicability in web text domains, it will be extended to other applications, like knowledge mining, bioinformatics, content-based, or collaborative info filtering, etc.
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