Journal
MACHINE LEARNING
Volume 45, Issue 2, Pages 219-228Publisher
SPRINGER
DOI: 10.1023/A:1010924920739
Keywords
clustering; data standardization; contingency coefficient; correlation ratio; weighting features; mixed-scale data
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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.
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