• DocumentCode
    3563603
  • Title

    Exclusive condition on item partition in fuzzy co-clustering based on K-L information regularization

  • Author

    Honda, Katsuhiro ; Chi-Hyon Oh ; Notsu, Akira

  • Author_Institution
    Grad. Sch. of Eng., Osaka Prefecture Univ., Sakai, Japan
  • fYear
    2014
  • Firstpage
    1413
  • Lastpage
    1417
  • Abstract
    FCCM based on K-L information regularization is an FCM-type co-clustering model, which is a fuzzy counterpart of the probabilistic Multinomial Mixture Models (MMMs). In MMMs and other FCM-type co-clustering models, whose goal is to simultaneously partition objects and items considering their mutual cooccurrence information, memberships of objects are forced to be exclusive in a similar way to FCM while item-memberships only represent the relative typicality in each cluster and are not forced to be exclusive. In this paper, a new co-clustering model is proposed by introducing the penalty for avoiding cluster overlapping in sequential fuzzy cluster extraction, which brings exclusive partition of items.
  • Keywords
    feature extraction; fuzzy set theory; mixture models; pattern clustering; probability; FCCM; K-L information regularization; MMM; fuzzy cluster extraction; fuzzy coclustering model; item partition; probabilistic multinomial mixture model; Clustering algorithms; Collaboration; Educational institutions; Electronic mail; Fuzzy systems; Linear programming; Probabilistic logic;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on
  • Type

    conf

  • DOI
    10.1109/SCIS-ISIS.2014.7044636
  • Filename
    7044636