A new validity measure for fuzzy c-means clustering
<|reference_start|>A new validity measure for fuzzy c-means clustering: 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.<|reference_end|>
@article{kim2024a,
    title={A new validity measure for fuzzy c-means clustering},
    author={Dae-Won Kim, Kwang H. Lee},
    journal={arXiv preprint arXiv:2407.06774},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06774},
    primaryClass={cs.AI}
}