• DocumentCode
    3585851
  • Title

    Comparison of major clustering algorithms using Weka tool

  • Author

    Gunasekara, R.P.T.H. ; Wijegunasekara, M.C. ; Dias, N.G.J.

  • Author_Institution
    Dept. of Comput. & Inf. Syst., Wayamba Univ. of Sri Lanka, Kuliyapitiya, Sri Lanka
  • fYear
    2014
  • Firstpage
    272
  • Lastpage
    272
  • Abstract
    Clustering algorithms are used in wide varieties of fields in many contexts. In these cases the behavior of the datasets are different to each other. Their sizes, density or the distribution may vary from one another. In data mining, clustering algorithms are implemented to build clusters with respect to a given dataset. But it is not an easy task to find the most suitable clustering algorithm for the given dataset. Therefore this study is done on several datasets using four clustering algorithms to identify the most suitable algorithm. This study is based on comparison of clustering data mining algorithms by using WEKA machine learning software.
  • Keywords
    data mining; learning (artificial intelligence); pattern clustering; WEKA machine learning software; clustering algorithms; data mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in ICT for Emerging Regions (ICTer), 2014 International Conference on
  • Print_ISBN
    978-1-4799-7731-4
  • Type

    conf

  • DOI
    10.1109/ICTER.2014.7083930
  • Filename
    7083930