A new validity measure for fuzzy c-means clustering

Dae-Won Kim, Kwang H. Lee
7/9/2024
cs.AI

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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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Cite this paper

@article{kim2024validity,
  title  = {A new validity measure for fuzzy c-means clustering},
  author = {Dae-Won Kim and Kwang H. Lee},
  year   = {2024},
  eprint = {2407.06774},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.06774v1}
}

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