EFFECTIVE LINEAR-TIME DOCUMENT CLUSTERING IN TEXT MINING USING WEB DOCUMENT CATEGORIZATION

Among data mining technique, clustering is one of the most important and traditional concept also an unsupervised learning paradigm. Similarity of a document pairs can be measured by matching of concepts. Finding or extracting the most relevant concept from the documents is a challengeable task. To address this issue, in this paper we introduce a concept of multi view point based similarity measure. Our proposed methods uses multiple point of reference between document pairs to extract more relevant match concept rather than extracting only ideas based on similarity measure. Using multiple view point, gathers more information about a particular topic from many different but relevant sources or concept. This strategy works well with smaller documents but is especially effective with longer documents. By gathering more relevant concepts from the documents with multiple points of reference, the document organization and retrieval can enhance the ability to make the most use of the documents held in storage and make retrieval of ideas as well as relevant task or concept much easier and faster. Experimental results shows that our proposed method efficiently extract more relevant concept.