EXPLOITING N-GRAM IMPORTANCE AND WIKIPEDIA BASED ADDITIONAL KNOWLEDGE FOR IMPROVEMENTS IN GAAC BASED DOCUMENT CLUSTERING

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 a many 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.