# A new approach for evaluating internal cluster validation indices

arXiv: https://arxiv.org/abs/2308.03894 · HTML: https://mlsanity.com/paper/2308.03894/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [Are Cluster Validity Measures (In)valid?](https://mlsanity.com/paper/2208.01261/) (2022)
- [Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes](https://mlsanity.com/paper/2002.01822/) (2020)

## Abstract (arXiv)

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.