# A New Index for Clustering Evaluation Based on Density Estimation

arXiv: https://arxiv.org/abs/2207.01294 · HTML: https://mlsanity.com/paper/2207.01294/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 29 references that are in the map.
- First posted on arXiv 4 Jul 2022.

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

- [Clustering with Neural Network and Index](https://mlsanity.com/paper/2212.03853/) (2022)
- [Min-Max-Jump distance and its applications](https://mlsanity.com/paper/2301.05994/) (2023)
- [Robust K-means Clustering using the Density Power Divergence Measure](https://mlsanity.com/paper/2608.30093/) (2026)

## Abstract (arXiv)

A new index for internal evaluation of clustering is introduced. The index is defined as a mixture of two sub-indices. The first sub-index  Ia  is called the Ambiguous Index; the second sub-index  Is  is called the Similarity Index. Calculation of the two sub-indices is based on density estimation to each cluster of a partition of the data. An experiment is conducted to test the performance of the new index, and compared with six other internal clustering evaluation indices -- Calinski-Harabasz index, Silhouette coefficient, Davies-Bouldin index, CDbw, DBCV, and VIASCKDE, on a set of 145 datasets. The result shows the new index significantly improves other internal clustering evaluation indices.