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Text Document Clustering Approach Using Document Similarity Measure With K Means Algorithm

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Text Document Clustering Approach Using Document Similarity Measure with K-Means Algorithm Mr. Ashish P. Mohod1 , Mr. Najim A. Sheikh2 , Mr. Bharat S. Dhak3 , Ms. Mausami Sawarkar4 Asst. Professor, CSE, Priyadarshini J.L.C.E, Nagpur,RTMNU, Maharashtra, India Email – mohod.ashish@gmail.com1,sheikhnajim4@gmail.com2,bharat.dhak@gmail.com3, mausami_sawarkar@rediffmail.com4 Abstract: Document clustering is way of automatic organization of documents into clusters so that documents within a cluster have high similarity in comparison to documents in other clusters. It is measuring similarity between documents and grouping similar documents together. The study of similarity measure for clustering is initially motivated by a research on automated text categorization. There was several similarity measures used for document similarity. It provides client representation and visualization of the documents; thus helps in easy navigation also. It has been used intensively because of its wide applicability in various areas such as web mining, search engines, and information retrieval. The key of organizing data in such a way is to improve data availability and to fasten data access, so that web information retrieval and content delivery on the web are improved. The main idea is to improve the accessibility and usability of text mining for various applications. By optimizing similarity measures the optimal clusters can be formed thus performance is improved. 1.

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