@misc{cogprints7148, month = {October}, title = {EXPLOITING N-GRAM IMPORTANCE AND ADDITIONAL KNOWEDGE BASED ON WIKIPEDIA FOR IMPROVEMENTS IN GAAC BASED DOCUMENT CLUSTERING}, author = {Mr. Niraj Kumar and Mr. Venkata Vinay Babu Vemula and Dr. Kannan Srinathan and Dr. Vasudeva Varma}, year = {2010}, keywords = {Document clustering, Group-average agglomerative clustering, Community detection, Similarity measure, N-gram, Wikipedia based additional knowledge.}, url = {http://cogprints.org/7148/}, abstract = {This paper provides a solution to the issue: ?How can we use Wikipedia based concepts in document clustering with lesser human involvement, accompanied by effective improvements in result?? In the devised system, we propose a method to exploit the importance of N-grams in a document and use Wikipedia based additional knowledge for GAAC based document clustering. The importance of N-grams in a document depends on several features including, but not limited to: frequency, position of their occurrence in a sentence and the position of the sentence in which they occur, in the document. First, we introduce a new similarity measure, which takes the weighted N-gram importance into account, in the calculation of similarity measure while performing document clustering. As a result, the chances of topical similarity in clustering are improved. Second, we use Wikipedia as an additional knowledge base both, to remove noisy entries from the extracted N-grams and to reduce the information gap between N-grams that are conceptually-related, which do not have a match owing to differences in writing scheme or strategies. Our experimental results on the publicly available text dataset clearly show that our devised system has a significant improvement in performance over bag-of-words based state-of-the-art systems in this area.} }