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.
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