A new approach for evaluating internal cluster validation indices

Zoltán Botta-Dukát
8/2/2023
cs.LGstat.ML

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Abstract

A vast number of different methods are available for unsupervised classification. Since no algorithm and parameter setting performs best in all types of data, there is a need for cluster validation to select the actually best-performing algorithm. Several indices were proposed for this purpose without using any additional (external) information. These internal validation indices can be evaluated by applying them to classifications of datasets with a known cluster structure. Evaluation approaches differ in how they use the information on the ground-truth classification. This paper reviews these approaches, considering their advantages and disadvantages, and then suggests a new approach.

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Cite this paper

@article{bottadukt2023approach,
  title  = {A new approach for evaluating internal cluster validation indices},
  author = {Zoltán Botta-Dukát},
  year   = {2023},
  eprint = {2308.03894},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.03894v1}
}

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