AN EXPANSION OF X-MEANS : PROGRESSIVE ITERATION OF K-MEANS AND MERGING OF THE CLUSTERS

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  • Ishioka Tsunenori
    Dept. of Applied Statistics and Measurement, Research Division, The National Center for University Entrance Examinations

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Other Title
  • x-means法改良の一提案 : k-means法の逐次繰り返しとクラスターの再併合
  • x meansホウ カイリョウ ノ イチ テイアン k meansホウ ノ チクジ クリカエシ ト クラスター ノ サイヘイゴウ

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We expand a non-hierarchical clustering algorithm that can determine the optimal number of clusters by using iterations of k-means and a stopping rule based on BIG. The procedure requires merging the clusters that a k-means iteration has made to avoid unsuitable division caused by the division order. By using this additional merging operation, the case of adequate clustering was increased for various types of simulation runs. With no prior information about the number of clusters, our method can get the optimal clustering based on information theory instead of on a heuristic method. The computational complexity of our method is ο(N log k) for the sample size N and the number of final clusters, k.

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