A fast self-organizing map algorithm by using genetic selection

  • Authors:
  • He Ni

  • Affiliations:
  • School of Finance, Zhejiang Gongshang University, HangZhou, P.R. China

  • Venue:
  • IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
  • Year:
  • 2009

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Abstract

Self-organizing feature map is able to represent the topological structure of the input data in a lower dimensional space, but however, at the cost of a huge amount of iterations. This paper presents an efficient approach to refining input data before it has been presented to forming the feature map. By using a data pre-processing inspired by the genetic selection, the improved self-organizing map algorithm can converge faster than the conventional self-organizing map in data clustering. Two sets of data are used to show the performance of the proposed algorithm.