Unsupervised Approach for Document Clustering Using Modified Fuzzy C mean Algorithm

International Journal of Computer & Organization Trends (IJCOT)          
© 2011 by IJCOT Journal
Volume-1 Issue-3                          
Year of Publication : 2011
Authors : K.Sathiyakumari , V.Preamsudha , G.Manimekalai


K.Sathiyakumari , V.Preamsudha , G.Manimekalai."Unsupervised Approach for Document Clustering Using Modified Fuzzy C mean Algorithm" International Journal of Computer & organization (IJCOT), V1(3):11-15 Nov - Dec 2011, ISSN 2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.


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 clu ster 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 whe n compared to the existing technique .


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Data mining, M FCM algorithm, Purity, Entropy, TF - IDF.