Paper Details

Text document classification and indexing is the major part of document management, where every documentshould be identified by its key terms and domain knowledge. Based on the domain knowledge, the documents are classifiedinto different classes. For document classification there are several approaches were proposed in existing system. But theexisting system is either term based or pattern based. And those systems suffered from polysemy and synonymy problems.To make a revolution in this challenging issue, the proposed system presents an innovative model for relevancefeature discovery and document classification. It discovers both positive and negative patterns in text documents as higherlevel features and deploys them over low-level features (terms). It also detects the most appropriate features based on itsweight and semantic nature and performs the document classification. Using this approach, the document index terms,patterns and category can be identified easily. In order to perform the above, a hybrid approach is used which contains thefollowing algorithms. (a). sequential semantic pattern mining algorithm for sequential pattern extraction (b). SemanticWeighted feature ranking algorithm to rank the higher supported terms in the form of semantic and patterns. (c). a Rulebased ontology concept which helps to classify the documents under various classes and helps to detect the possible indexterms. This helps to reduce the training data collection problems.

S.Nithya, Mr. N.Kamalra, “PREEMINENT FEATURE DISCOVERY AND DOCUMENT CLUSTERING USING TEXT MINING”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 3, Issue 11, pp. 184-196, November 2016.