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dc.contributor.authorK, Sathiyakumari-
dc.contributor.authorV, Pream Sudha-
dc.contributor.authorG, Manimekalai-
dc.date.accessioned2020-12-22T05:46:08Z-
dc.date.available2020-12-22T05:46:08Z-
dc.date.issued2011-11-
dc.identifier.issn2249-2593-
dc.identifier.urihttps://pdfs.semanticscholar.org/60b4/6ad994ba917dec8b52966d4ae659375b9e7c.pdf-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/2333-
dc.description.abstractClustering 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 cluring 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 techniqueen_US
dc.language.isoenen_US
dc.publisherInternational Journal of Computer& Organization Trendsen_US
dc.subjectData miningen_US
dc.subjectMFCM algorithmen_US
dc.subjectPurityen_US
dc.subjectEntropyen_US
dc.subjectTF-IDFen_US
dc.titleUNSUPERVISED APPROACH FOR DOCUMENT CLUSTERING USING MODIFIED FUZZY C MEAN ALGORITHMen_US
dc.typeArticleen_US
Appears in Collections:International Journals

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