# A new nonparametric interpoint distance-based measure for assessment of clustering

arXiv: https://arxiv.org/abs/2210.08972 · HTML: https://mlsanity.com/paper/2210.08972/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 46 references that are in the map.
- First posted on arXiv 1 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [A new measure for assessment of clustering based on kernel density estimation](https://mlsanity.com/paper/2201.02030/) (2022)

## Built on by (most cited first, 3 of 3)

- [Confirmation of Binary Clustering in Gamma-Ray Bursts through an Integrated p-value from Multiple Nonparametric Tests of Hypotheses](https://mlsanity.com/paper/2605.04739/) (2026)
- [Quality check of a sample partition using multinomial distribution](https://mlsanity.com/paper/2404.07778/) (2024)
- [A new completely parameter-free clustering algorithm for unsupervised classification of BATSE gamma-ray bursts](https://mlsanity.com/paper/2605.30175/) (2026)

## Abstract (arXiv)

A new interpoint distance-based measure is proposed to identify the optimal number of clusters present in a data set. Designed in nonparametric approach, it is independent of the distribution of given data. Interpoint distances between the data members make our cluster validity index applicable to univariate and multivariate data measured on arbitrary scales, or having observations in any dimensional space where the number of study variables can be even larger than the sample size. Our proposed criterion is compatible with any clustering algorithm, and can be used to determine the unknown number of clusters or to assess the quality of the resulting clusters for a data set. Demonstration through synthetic and real-life data establishes its superiority over the well-known clustering accuracy measures of the literature.