• DocumentCode
    548028
  • Title

    Fuzzy improved genetic k-means algorithm

  • Author

    Bozorgnia, A. ; Zargar, Samaneh Hajy Mahdizadeh ; Yaghmaee, Mohammad Hossein

  • Author_Institution
    Dept. of Comput. Eng., Islamic Azad Univ., Mashhad, Iran
  • fYear
    2011
  • fDate
    17-19 May 2011
  • Firstpage
    1
  • Lastpage
    1
  • Abstract
    Clustering is a significant technique in data mining. Many methods for increasing the ability of clustering large data have been presented, one appropriate technique is the k-means method which has been combined with Artificial intelligence methods like genetic algorithm and has created an optimal performance. In primary clustering algorithms the clustering result depends on the initial centers of the clusters. In the presentation of an optimized clustering technique, apart from considering the current issues of clustering, it has tried to find the optimized number of clusters in the clustering procedure. The proposed technique increases the performance and integration of the k-means genetic algorithm with the use of fuzzy methods.
  • Keywords
    artificial intelligence; data mining; fuzzy set theory; genetic algorithms; pattern clustering; artificial intelligence; data clustering; data mining; fuzzy improved genetic k-means algorithm; fitness factor; fuzzy method; genetic algorithm; k-means algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Engineering (ICEE), 2011 19th Iranian Conference on
  • Conference_Location
    Tehran
  • Print_ISBN
    978-1-4577-0730-8
  • Type

    conf

  • Filename
    5955918