• DocumentCode
    3277970
  • Title

    Constrained ant clustering

  • Author

    Xu, Xiao-Hua ; Pan, Zhou-Jin ; He, Ping ; Chen, Ling

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Yangzhou Univ., Yangzhou, China
  • Volume
    4
  • fYear
    2011
  • fDate
    10-13 July 2011
  • Firstpage
    1566
  • Lastpage
    1570
  • Abstract
    By simulating the clustering behavior of the real-world ant colonies, we propose in this paper a constrained ant clustering algorithm based on random walk to deal with the constrained clustering problems with pairwise must-link and cannot-link constraints. Experimental results show that our approach is more effective on both synthetic datasets and UCI datasets compared with the cop-kmeans algorithm and ant-based clustering algorithm.
  • Keywords
    data analysis; pattern clustering; UCI datasets; clustering behavior; constrained ant clustering; constrained clustering problems; cop-k means algorithm; data analysis; synthetic datasets; Algorithm design and analysis; Clustering algorithms; Cybernetics; Educational institutions; Machine learning; Machine learning algorithms; Particle swarm optimization; Ant clustering; Constrained clustering; Random walk;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2011 International Conference on
  • Conference_Location
    Guilin
  • ISSN
    2160-133X
  • Print_ISBN
    978-1-4577-0305-8
  • Type

    conf

  • DOI
    10.1109/ICMLC.2011.6016967
  • Filename
    6016967
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3277970