Reinterpreting the Category Utility Function

  • Mirkin B
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Abstract

The category utility function is a partition quality scoring function applied in some clustering programs of machine learning. We reinterpret this function in terms of the data variance explained by a clustering, or, equivalently, in terms of the square-error classical clustering criterion that administers the K-Means and Ward methods. This analysis suggests extensions of the scoring function to situations with differently standardized and mixed scale data.

Author-supplied keywords

  • Clustering
  • Contingency coefficient
  • Correlation ratio
  • Data standardization
  • Mixed-scale data
  • Weighting features

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