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arXiv · 2407.06774

A new validity measure for fuzzy c-means clustering

Dae-Won Kim·Kwang H. Lee
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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.

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Dae-Won Kim, Kwang H. Lee. 2024-07-09. A new validity measure for fuzzy c-means clustering. https://arxiv.org/abs/2407.06774

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