• DocumentCode
    3261411
  • Title

    Classification and Clustering: A Perspective toward Risk Mining

  • Author

    Miyamoto, Sadaaki

  • Author_Institution
    Dept. of Risk Eng., Tsukuba Univ., Ibaraki
  • fYear
    2006
  • fDate
    Dec. 2006
  • Firstpage
    726
  • Lastpage
    730
  • Abstract
    In this paper three topics concerning data clustering are discussed. One is an association found in supervised classification and clustering, whereby new techniques of clustering can be developed some of which are described here. Next two are concerned with risk mining. The consideration of associations between the above two classes of methods is related to the second topic in which clustering in the presence of rough sets is considered. The third is related to mining actual data on a risk issue, where the method of fuzzy clustering is applied. Throughout these considerations, we focus more on a research perspective, i.e., possibilities in near future rather than an established method or algorithm
  • Keywords
    data mining; fuzzy set theory; learning (artificial intelligence); regression analysis; risk analysis; rough set theory; data clustering; fuzzy clustering; research perspective; risk mining; rough sets; supervised classification; supervised clustering; Clustering algorithms; Data engineering; Data mining; Fuzzy sets; Logic; Nearest neighbor searches; Predictive models; Risk analysis; Rough sets; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    0-7695-2702-7
  • Type

    conf

  • DOI
    10.1109/ICDMW.2006.41
  • Filename
    4063721