• DocumentCode
    3372923
  • Title

    Stochastic on-line algorithm versus batch algorithm for quantization and self organizing maps

  • Author

    Fort, Jean-Claude ; Cottrell, Marie ; Letremy, Patrick

  • Author_Institution
    Inst. Elie Cartan, Univ. Nancy 1, Vandoeuvre-les-Nancy, France
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    43
  • Lastpage
    52
  • Abstract
    The Kohonen algorithm (SOM) was originally designed as a stochastic algorithm which works in an on-line way and which was designed to model some adaptative features of the human brain. In fact it is nowadays extensively used for data mining, data visualization, and exploratory data analysis. Some users are tempted to use the batch version of the Kohonen algorithm since it is a deterministic algorithm which can be convenient if one needs to get reproducible results and which can go faster in some cases. In this paper, we try to elucidate the mathematical nature of this batch variant and give some elements of comparison of both algorithms. Then we compare both versions on a real data set
  • Keywords
    self-organising feature maps; Kohonen algorithm; SOM; batch version; data mining; data visualization; exploratory data analysis; stochastic algorithm; Algorithm design and analysis; Brain modeling; Data analysis; Data mining; Euclidean distance; Humans; Quantization; Self organizing feature maps; Signal processing algorithms; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop
  • Conference_Location
    North Falmouth, MA
  • ISSN
    1089-3555
  • Print_ISBN
    0-7803-7196-8
  • Type

    conf

  • DOI
    10.1109/NNSP.2001.943109
  • Filename
    943109