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Reinterpreting the category utility function
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DOI：10.1023/A:1010924920739.png)
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Abstract
En 中文
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.
Keywords:
clustering
data standardization
contingency coefficient
correlation ratio
weighting features
mixed-scale data
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