• DocumentCode
    2677462
  • Title

    Document clustering by fuzzy c-mean algorithm

  • Author

    Win, Thaung Thaung ; Mon, Lin

  • Author_Institution
    Univ. of Comput. Studies Mandalay, UCSM, Yangon, Myanmar
  • Volume
    1
  • fYear
    2010
  • fDate
    27-29 March 2010
  • Firstpage
    239
  • Lastpage
    242
  • Abstract
    Clustering documents enable the user to have a good overall view of the information contained in the documents. Most classical clustering algorithms assign each data to exactly one cluster, thus forming a crisp partition of the given data, but fuzzy clustering allows for degrees of membership, to which a data belongs to different clusters. In this system, documents are clustered by using fuzzy c-means (FCM) clustering algorithm. FCM clustering is one of well-know unsupervised clustering techniques. However FCM algorithm requires the user to pre-define the number of clusters and different values of clusters corresponds to different fuzzy partitions. So the validation of clustering result is needed. PBM index and F-measure are used for cluster validity.
  • Keywords
    document handling; fuzzy set theory; pattern clustering; F-measurement; PBM index; document clustering; fuzzy c-mean algorithm; fuzzy partitions; unsupervised clustering techniques; Algorithm design and analysis; Clustering algorithms; Clustering methods; Fuzzy sets; Fuzzy systems; Information retrieval; Navigation; Partitioning algorithms; Spatial databases; Topology; Clulster Validity; Document Clustering; Fuzzy c-mean algorithm; PBMindex;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computer Control (ICACC), 2010 2nd International Conference on
  • Conference_Location
    Shenyang
  • Print_ISBN
    978-1-4244-5845-5
  • Type

    conf

  • DOI
    10.1109/ICACC.2010.5487022
  • Filename
    5487022