Integration of Ant Colony SOM and K-Means for Clustering Analysis

  title={Integration of Ant Colony SOM and K-Means for Clustering Analysis},
  author={Sheng-Chai Chi and Chih Chieh Yang},
In data analysis techniques, the capability of SOM and K-means for clustering large-scale databases has already been confirmed. The most remarkable advantage of SOM-based two-stage methods is in saving time without the considerable computations required by conventional clustering methods for large and complicated data sets. In this research, we propose and evaluate a two-stage clustering method, which combines an ant-based SOM and K-means. The ant-based SOM clustering model, ABSOM, embeds the… CONTINUE READING
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  • A. Sugiyama, M. Kotani
  • Proc. of Int. J. Conf. on Neural Networks
  • 2002
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  • R. J. Kuo, S. C. Chi, P. W. Teng
  • Computers and Industrial Engineering, 4
  • 2001
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