• Zete Sonali Sahebrao
• Rajole Rohini Vasant
• Deshmukh Shital Pramod
• Tile Swati Bhaskar

Document clustering involves the use of descriptors and descriptor extraction. Descriptors are sets of words that describe the contents within the cluster. Document clustering is generally considered to be a centralized process. Examples of document clustering include web document clustering for search users. Finding the appropriate number of clusters to which documents should be partitioned is crucial in document clustering. In this will focus on various clustering techniques and our proposed system to discover the cluster structure without requiring the number of clusters as input. Document features or even we can say that the various attributes will be automatically partitioned into two groups, in particular, discriminative words and non- discriminative words, and contribute differently to document clustering. There is one variation inference algorithm which have studied to infer the document collection structure as well as the partition of document words at the same time. Will justify at the end in the conclusion how our approach will perform well on the data set. Then will justify our systems accuracy and efficiency by describing what we have proposed with the predicted modules and features for the system.

Database management, database applications-text mining, pattern recognition,ss Clustering, document clustering, feature partition

"Content and Feature Based Document Clustering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.2, Issue 3, page no.813-818, March-2015, Available :http://www.jetir.org/papers/JETIR1503083.pdf

"Content and Feature Based Document Clustering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.2, Issue 3, page no. pp813-818, March-2015, Available at : http://www.jetir.org/papers/JETIR1503083.pdf