A new validity measure for fuzzy c-means clustering
Artificial Intelligence
2024-07-10 v1
Abstract
A new cluster validity index is proposed for fuzzy clusters obtained from fuzzy c-means algorithm. The proposed validity index exploits inter-cluster proximity between fuzzy clusters. Inter-cluster proximity is used to measure the degree of overlap between clusters. A low proximity value refers to well-partitioned clusters. The best fuzzy c-partition is obtained by minimizing inter-cluster proximity with respect to c. Well-known data sets are tested to show the effectiveness and reliability of the proposed index.
Keywords
Cite
@article{arxiv.2407.06774,
title = {A new validity measure for fuzzy c-means clustering},
author = {Dae-Won Kim and Kwang H. Lee},
journal= {arXiv preprint arXiv:2407.06774},
year = {2024}
}
Comments
Accepted at FIP-2002