# Cluster validity index based on Jeffrey divergence

## TL;DR

This paper introduces a new cluster validity index utilizing Jeffrey divergence for measuring separation, which outperforms traditional indexes in various data scenarios.

## Contribution

The paper proposes a novel cluster validity index based on Jeffrey divergence, improving the accuracy of cluster separation measurement.

## Key findings

- The new index outperforms existing indexes in experiments.
- Jeffrey divergence provides a more reliable separation measure.
- The index is effective across different data types.

## Abstract

Cluster validity indexes are very important tools designed for two purposes: comparing the performance of clustering algorithms and determining the number of clusters that best fits the data. These indexes are in general constructed by combining a measure of compactness and a measure of separation. A classical measure of compactness is the variance. As for separation, the distance between cluster centers is used. However, such a distance does not always reflect the quality of the partition between clusters and sometimes gives misleading results. In this paper, we propose a new cluster validity index for which Jeffrey divergence is used to measure separation between clusters. Experimental results are conducted using different types of data and comparison with widely used cluster validity indexes demonstrates the outperformance of the proposed index.