Department of Computer Science (PG)

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UNSUPERVISED APPROACH FOR DOCUMENT CLUSTERING USING MODIFIED FUZZY C MEAN ALGORITHM
(International Journal of Computer & Organization Trends, 2011) G, Manimekalai; V, Preamsudha; K, Sathiyakumari
Clustering is one the main area in data mining literature. There are various algorithms for clustering. There are several clustering approaches available in the literature to cluster the document. But most of the existing clustering techniques suffer from a wide range of limitations. The existing clustering approaches face the issues like practical applicability, very less accuracy, more classification time etc. In recent times, inclusion of fuzzy logic in clustering results in better clustering results. One of the widely used fuzzy logic based clustering is Fuzzy C-Means (FCM) Clustering. In order to further improve the performance of clustering, this thesis uses Modified Fuzzy C-Means (MFCM) Clustering. Before clustering, the documents are ranked using Term Frequency–Inverse Document Frequency (TF–IDF) technique. From the experimental results, it can be observed that the proposed technique results in better clustering results when compared to the existing technique