A new validity measure for fuzzy c-means clustering

TL;DR

This paper introduces a novel validity index for fuzzy c-means clustering that assesses cluster overlap by measuring inter-cluster proximity, improving cluster evaluation accuracy.

Contribution

The paper proposes a new validity index based on inter-cluster proximity to better evaluate fuzzy c-means clustering results.

Findings

The index effectively distinguishes well-partitioned clusters.

Testing on standard datasets confirms the index's reliability.

Minimizing the index yields optimal fuzzy partitions.

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.