• DocumentCode
    2801953
  • Title

    Novel Hybrid Document Clustering Algorithm Based on Ant Colony and Agglomerate

  • Author

    Wang, Xiaohua ; Shen, Jie ; Tang, Hongjun

  • Author_Institution
    Inst. of Comput. Applic. Technol., Hangzhou Dianzi Univ., Hangzhou, China
  • Volume
    3
  • fYear
    2009
  • fDate
    Nov. 30 2009-Dec. 1 2009
  • Firstpage
    65
  • Lastpage
    68
  • Abstract
    In this paper, ant colony algorithm was improved from two aspects, then a novel hybrid ant colony and agglomerate document clustering algorithm, hybrid-AC&A, has been proposed based on ant colony model and agglomerate clustering algorithms. Firstly, Compact algorithm was applied while ant dropping its load. Secondly, evaluate function based schedule algorithm was applied while ant obtains load. Finally, agglomerate clustering algorithm was integrated into the iteration procedure of ant colony clustering algorithm. The performance of Hybrid-AC&A is compared with other clustering methods, the experimental results denote that the proposed algorithm not only inherits the intrinsic advantages of ant colony model clustering algorithm, but also improves the aspect of time efficiency. Computational result on real documents collection shows it is much more efficient than other mentioned algorithms.
  • Keywords
    document handling; optimisation; agglomerate clustering algorithm; ant colony algorithm; function based schedule algorithm; hybrid document clustering algorithm; Algorithm design and analysis; Ant colony optimization; Biological system modeling; Clustering algorithms; Clustering methods; Computer applications; Convergence; Density measurement; Knowledge acquisition; Scattering; Agglomerate; Ant Colony; Document Clustering;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3888-4
  • Type

    conf

  • DOI
    10.1109/KAM.2009.182
  • Filename
    5362451