• DocumentCode
    2866491
  • Title

    Mining quantitative frequent itemsets using adaptive density-based subspace clustering

  • Author

    Washio, Takashi ; Mitsunaga, Yuki ; Motoda, Hiroshi

  • Author_Institution
    Inst. for Sci. & Ind. Res., Osaka Univ., Japan
  • fYear
    2005
  • fDate
    27-30 Nov. 2005
  • Abstract
    A novel approach to subspace clustering is proposed to exhaustively and efficiently mine quantitative frequent item-sets (QFIs) from massive transaction data. For the computational tractability, our approach introduces adaptive density-based and Apriori-like algorithm. Its outstanding performance is shown through numerical experiments.
  • Keywords
    data mining; pattern clustering; adaptive density-based subspace clustering; computational tractability; massive transaction data; quantitative frequent item set mining; Association rules; Cities and towns; Clustering algorithms; Computational complexity; Data mining; Hypercubes; Itemsets; Merging; Mining industry; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining, Fifth IEEE International Conference on
  • ISSN
    1550-4786
  • Print_ISBN
    0-7695-2278-5
  • Type

    conf

  • DOI
    10.1109/ICDM.2005.100
  • Filename
    1565784
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2866491