A new validity measure for fuzzy c-means clustering
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866909248441548800 |
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| author | Kim, Dae-Won Lee, Kwang H. |
| author_facet | Kim, Dae-Won Lee, Kwang H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06774 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A new validity measure for fuzzy c-means clustering Kim, Dae-Won Lee, Kwang H. Artificial Intelligence 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. |
| title | A new validity measure for fuzzy c-means clustering |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2407.06774 |