ML Anthology

A Nonparametric Valley-Seeking Technique for Cluster Analysis

Warren L. G. Koontz, Keinosuke Fukunaga
IJCAI 1971 pp. 411-417
doi:10.1109/TC.1972.5008922 /ijcai/1971/koontz1971ijcai-nonparametric/

Abstract

The problem of clustering multivariate observations is viewed as the replacement of a set of vectors with a set of labels and representative vectors. A general criterion for clustering is derived as a measure of representation error. Some special cases are derived by simplifying the general criterion. A general algorithm for finding the optimum classification with respect to a given criterion is derived. For a particular case, the algorithm reduces to a repeated application of a straightforward decision rule which behaves as a valley-seeking technique. Asymptotic properties of the procedure are developed. Numerical examples are presented for the finite sample case.

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Cite

Text

Koontz and Fukunaga. "A Nonparametric Valley-Seeking Technique for Cluster Analysis." International Joint Conference on Artificial Intelligence, 1971. doi:10.1109/TC.1972.5008922

Markdown

[Koontz and Fukunaga. "A Nonparametric Valley-Seeking Technique for Cluster Analysis." International Joint Conference on Artificial Intelligence, 1971.](https://mlanthology.org/ijcai/1971/koontz1971ijcai-nonparametric/) doi:10.1109/TC.1972.5008922

BibTeX

@inproceedings{koontz1971ijcai-nonparametric,
  title     = {{A Nonparametric Valley-Seeking Technique for Cluster Analysis}},
  author    = {Koontz, Warren L. G. and Fukunaga, Keinosuke},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {1971},
  pages     = {411-417},
  doi       = {10.1109/TC.1972.5008922},
  url       = {https://mlanthology.org/ijcai/1971/koontz1971ijcai-nonparametric/}
}