DOCUMENT CLUSTERING USING STATISTICAL INTEGRATED GRAPH BASED CO-WORD INTERPRETATION APPROACH

The co-word extraction method using a statistical integrated graph based approach has been adopted, and the purpose of this work is to extract the most important cowords from the various documents. In the new digital age of document summarization, automatic extraction of co-words from documents is very important. For example, instead of looking at the entire document, the co-words assigned by the author partially point to the discussion of the document. Nevertheless, these co-words are not enough to define the comprehensive meaning of the document. Although there are many approaches available, accurate automatic co-word extraction remains a major challenge in the areas of natural language processing, text mining, and information retrieval. In this paper, first an integrated graph-based method and standard statistical approach is proposed to extract the co-words from the documents. Further, the documents clustering are carried out by implementing a novel statistical integrated graph based co-word interpretation approach. The simulation results reveal that the proposed statistical integrated graph based co-word interpretation approach obtained the best results for clustering the text documents. A comprehensive analysis of the proposed statistical integrated graph based co-word interpretation approach is performed on hundreds of articles to verify its performance.