# A new completely parameter-free clustering algorithm for unsupervised classification of BATSE gamma-ray bursts

arXiv: https://arxiv.org/abs/2605.30175 · HTML: https://mlsanity.com/paper/2605.30175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 117 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 5 of 5)

- [A new measure for assessment of clustering based on kernel density estimation](https://mlsanity.com/paper/2201.02030/) (2022)
- [A new nonparametric interpoint distance-based measure for assessment of clustering](https://mlsanity.com/paper/2210.08972/) (2022)
- [A new interpoint distance-based clustering algorithm using kernel density estimation](https://mlsanity.com/paper/2305.00901/) (2023)
- [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)
- [Evaluation of the number of clusters in a data set using p-values from Multiple Tests of Hypotheses](https://mlsanity.com/paper/2605.20806/) (2026)

## Abstract (arXiv)

Cluster analysis is a widely applied machine learning technique to understand the existing patterns in the population of gamma-ray bursts (GRBs), in order to explore their physical sources. In the present scenario, the number of clusters corresponding to differentiable groups is still under conflict, in spite of numerous attempts with the state-of-the-art clustering procedures. This crucial unknown parameter needs to be evaluated, either directly or indirectly in terms of other tuning parameters, to produce the clusters in GRBs through implementation of an appropriate clustering algorithm. While most of the applied algorithms reached two physically explained groups of merger and collapsar predominated by the short and long bursts respectively, other statistical approaches violated this binary partition. However, physical establishment of any additional cluster(s) is not yet confirmed. Therefore, we propose a new algorithm, from a different stream of clustering referred to as `completely parameter-free', which carries out the classification of GRBs in a manner that has not been tried so far. It indicates two main groups, of short and long duration bursts from the BATSE sample, compatible with the merger-collapsar theory.