# Evaluation of the number of clusters in a data set using p-values from Multiple Tests of Hypotheses

arXiv: https://arxiv.org/abs/2605.20806 · HTML: https://mlsanity.com/paper/2605.20806/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2026.

## Built on by (most cited first, 1 of 1)

- [A new completely parameter-free clustering algorithm for unsupervised classification of BATSE gamma-ray bursts](https://mlsanity.com/paper/2605.30175/) (2026)

## Abstract (arXiv)

This paper proposes a novel, nonparametric, interpoint distance-based measure to investigate whether there exist any groups in a set of given data, and if so then, how many groups are prevailing in total. It is a cluster accuracy index useful for arbitrary-dimensional data set, in association with any clustering algorithm having the number of groups specified as a priori. We perform univariate, nonparametric, multiple statistical tests of hypotheses, where as many dependent tests as the sample size are carried out using the interpoint distances. They possess p-values to be combined to reach a decision, which is taken in a step-wise process for a possible number of clusters. It reduces the unnecessary computations compared with the other accuracy measures from the literature. Data study establishes the proposed index's efficiency and superiority.