Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm
For the question that fuzzy c-means （FCM） clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima, this paper introduces a new metric norm in FCM and particle swarm optimization （PSO） clustering algorithm, and proposes a par...
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| Veröffentlicht in: | Shanghai jiao tong da xue xue bao 2015-02, Vol.20 (1), p.51-55 |
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| container_title | Shanghai jiao tong da xue xue bao |
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| creator | 毛力 宋益春 李引 杨弘 肖炜 |
| description | For the question that fuzzy c-means （FCM） clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima, this paper introduces a new metric norm in FCM and particle swarm optimization （PSO） clustering algorithm, and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined experiment shows that the AF-APSO can avoid local optima, significantly. with particle swarm optimization （AF-APSO）. The and get the best fitness and clustering performance |
| doi_str_mv | 10.1007/s12204-015-1587-x |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 1007-1172 |
| ispartof | Shanghai jiao tong da xue xue bao, 2015-02, Vol.20 (1), p.51-55 |
| issn | 1007-1172 1995-8188 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1709733572 |
| source | SpringerNature Complete Journals; Alma/SFX Local Collection |
| subjects | Algorithms Architecture Clustering Computer Science Electrical Engineering Engineering Fuzzy Fuzzy logic Fuzzy set theory Life Sciences Materials Science Norms Optimization Swarm intelligence |
| title | Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm |
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