Abstract

Document clustering is the technique used to group up the document with the reference to the similarity. It is widely used in web mining and digital library environment. Documents are represented in vector space model. Each document is a vector in the word space and each element of the vector indicates the frequency of the corresponding word in the document. Documents are presented as high dimensional data elements. It is a very complex task to cluster documents using K-means clustering algorithm. The sub space clustering schemes can be adopted to cluster documents. The document clustering uses the term weights from the similarity measure. The sub space model uses the relevant attributes for the similarity estimation. The fuzzy logic is used to cluster the documents. The fuzzy document clustering scheme is enhanced with semantic analysis mechanism. Semantic analysis is carried out with the support of the ontology. The ontology is used to maintain term relationships. Term relationships are represented using the synonym, meronym and hypernym factors. Ontology is manually collected by the users. Domain based ontology is used for the document clustering process. The system uses the data mining domain based ontology for the semantic analysis. Semantic weights are used in the similarity measure. Fuzzy based text document clustering scheme uses the stop word filters and stemming process under the document preprocess. Term clustering and semantic clustering operations are performed in the system.

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