• DocumentCode
    3532205
  • Title

    DiSCl: Distributed Intelligent Subspace Clustering, a density based clustering approach for very high dimensional distributed dataset

  • Author

    Jahirabadkar, Sunita ; Kulkarni, Parag

  • Author_Institution
    Cummins Coll. of Eng., Pune, India
  • fYear
    2009
  • fDate
    28-31 July 2009
  • Firstpage
    550
  • Lastpage
    551
  • Abstract
    In this paper, a problem called, ldquoDistributed Subspace Clustering for high dimensional distributed database, based on density notion of clusteringrdquo is explored. To solve this problem, we described our algorithm ISC (Intelligent Subspace Clustering) which uses the concept called Hierarchical Subspace Clustering. ISC finds the input parameter epsi i.e. the distance, required for any density based clustering, adaptively at various levels of dimensionalities. This gives the ability of incremental learning and dynamic inclusion and exclusions of subspaces which lead to better cluster formation.
  • Keywords
    deductive databases; distributed databases; learning (artificial intelligence); pattern clustering; distributed intelligent subspace clustering; hierarchical subspace clustering; high dimensional distributed database; incremental learning; Clustering algorithms; Couplings; Deductive databases; Distributed databases; Educational institutions; Testing; Cluster Density; Distributed Clustering; High dimensional data; Subspace Clustering;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Networked Digital Technologies, 2009. NDT '09. First International Conference on
  • Conference_Location
    Ostrava
  • Print_ISBN
    978-1-4244-4614-8
  • Electronic_ISBN
    978-1-4244-4615-5
  • Type

    conf

  • DOI
    10.1109/NDT.2009.5272086
  • Filename
    5272086