S-Divergence-Based Internal Clustering Validation Index

Authors

Krishna Kumar Sharma
Ayan Seal
Anis Yazidi
Ondrej Krejcar

DOI

10.9781/ijimai.2023.10.001

Abstract

A clustering validation index (CVI) is employed to evaluate an algorithm’s clustering results. Generally, CVI statistics can be split into three classes, namely internal, external, and relative cluster validations. Most of the existing internal CVIs were designed based on compactness (CM) and separation (SM). The distance between cluster centers is calculated by SM, whereas the CM measures the variance of the cluster. However, the SM between groups is not always captured accurately in highly overlapping classes. In this article, we devise a novel internal CVI that can be regarded as a complementary measure to the landscape of available internal CVIs. Initially, a database’s clusters are modeled as a non-parametric density function estimated using kernel

Source Publication

International Journal of Interactive Multimedia and Artificial Intelligence

Recommended Citation

Sharma, Krishna Kumar; Seal, Ayan; Yazidi, Anis; and Krejcar, Ondrej (2023) "S-Divergence-Based Internal Clustering Validation Index," International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 8: Iss. 4, Article 2.
DOI: 10.9781/ijimai.2023.10.001
Available at: https://ijimai.researchcommons.org/ijimai/vol8/iss4/2