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We present a method of combining a self-organizing map (SOM) and k-means clustering for analyzing and categorizing gene expression data. Some studies have addressed about visualizing cluster structures in an easily understandable manner using a U-matrix or Sammon's mapping. However, it is difficult to find obvious clustering boundaries in the SOM results. We show that the method is effective for categorizing the published data of yeast gene expression.