A novel cluster internal evaluation index based on hyper-balls

TL;DR

This paper introduces a new cluster internal evaluation index based on hyper-balls, which effectively assesses clustering quality and determines the optimal number of clusters, especially for noisy and arbitrarily shaped data.

Contribution

The paper proposes a novel HCVI index and a general method for optimal cluster number determination, improving evaluation accuracy over existing indices.

Findings

HCVI outperforms existing indices on synthetic data

Effective in noisy and arbitrarily shaped clusters

Provides a reliable method for optimal cluster determination

Abstract

It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis. In this paper, the multi-granularity characterization of the data set is carried out to obtain the hyper-balls. The cluster internal evaluation index based on hyper-balls(HCVI) is defined. Moreover, a general method for determining the optimal number of clusters based on HCVI is proposed. The proposed methods can evaluate the clustering results produced by the several classic methods and determine the optimal cluster number for data sets containing noises and clusters with arbitrary shapes. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.