• DocumentCode
    1806087
  • Title

    Research on a new clustering algorithm in data mining

  • Author

    Tan Zhongbing

  • Author_Institution
    Computer Science Department, Beijing Institute of Technology, Zhuhai, China
  • fYear
    2013
  • fDate
    1-8 Jan. 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Data mining is one of the leading fields in the combination area of database and decision supporting, and clustering is a significant task for data mining, in which clustering algorithm is the core technology. The new clustering method based on genetic algorithm and gradient descent method (G-G clustering algorithm) is proposed in this paper. Genetic algorithm has the advantages of global searching and strong robustness, and will not getting stuck at local optimal values. Unfortunately, it can only reach the near-optimal value after many generations of selection, crossover and mutation. Therefore, gradient descent method is utilized at the end of genetic algorithm based clustering method to get global optimal values. Clustering results of two groups of experimental data show that the new clustering method is one with global optimal, and the results is evidently better than k-means clustering method.
  • Keywords
    Algorithm design and analysis; Clustering algorithms; Wheels; clustering analysis; data mining; genetic algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Conference Anthology, IEEE
  • Conference_Location
    China
  • Type

    conf

  • DOI
    10.1109/ANTHOLOGY.2013.6784990
  • Filename
    6784990