ML Anthology

Reinterpreting the Category Utility Function

Boris G. Mirkin
MLJ 2001 pp. 219-228
doi:10.1023/A:1010924920739 /mlj/2001/mirkin2001mlj-reinterpreting/

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.

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Text

Mirkin. "Reinterpreting the Category Utility Function." Machine Learning, 2001. doi:10.1023/A:1010924920739

Markdown

[Mirkin. "Reinterpreting the Category Utility Function." Machine Learning, 2001.](https://mlanthology.org/mlj/2001/mirkin2001mlj-reinterpreting/) doi:10.1023/A:1010924920739

BibTeX

@article{mirkin2001mlj-reinterpreting,
  title     = {{Reinterpreting the Category Utility Function}},
  author    = {Mirkin, Boris G.},
  journal   = {Machine Learning},
  year      = {2001},
  pages     = {219-228},
  doi       = {10.1023/A:1010924920739},
  volume    = {45},
  url       = {https://mlanthology.org/mlj/2001/mirkin2001mlj-reinterpreting/}
}