A new approach for evaluating internal cluster validation indices

• URL: http://arxiv.org/abs/2308.03894v1
• Date: Wed, 2 Aug 2023 06:55:33 GMT
• Title: A new approach for evaluating internal cluster validation indices
• Authors: Zolt\'an Botta-Duk\'at
• Abstract summary: Cluster validation is needed to select the best-performing algorithm. Several indices were proposed for this purpose without using any additional (external) information. Evaluation approaches differ in how they use the information on the ground-truth classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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