• DocumentCode
    2113237
  • Title

    Clustering Ensemble Based on Hierarchical Partition

  • Author

    Li, Taoying ; Chen, Yan

  • Author_Institution
    Transp. Manage. Collage, Dalian Maritime Univ., Dalian, China
  • fYear
    2009
  • fDate
    20-22 Sept. 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Many clustering ensemble algorithms need to predesign initial thresholds before partition data points, which is supervised learning and directly influence the efficiency of clustering. In order to cluster data points under fully unsupervised situation, the hierarchical partition is introduced in this paper. The proposed algorithm makes use of the distribution of results of all clustering memberships by constructing the m-subset of Descartes with the support degree. The theorems and definitions advanced in this paper are detailed proved. Finally, the proposed algorithm is applied in practice and results show that it is effective.
  • Keywords
    hierarchical systems; learning (artificial intelligence); pattern clustering; set theory; Descartes m-subset; clustering ensemble algorithm; hierarchical partition; partition data point; predesign initial threshold; supervised learning; Algorithm design and analysis; Assembly; Clustering algorithms; Data mining; Information processing; Nearest neighbor searches; Partitioning algorithms; Supervised learning; Text recognition; Transportation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management and Service Science, 2009. MASS '09. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-4638-4
  • Electronic_ISBN
    978-1-4244-4639-1
  • Type

    conf

  • DOI
    10.1109/ICMSS.2009.5302536
  • Filename
    5302536