Multistage random sampling genetic-algorithm-based fuzzy c-means clustering algorithm

This work presents a multistage random sampling genetic-algorithm-based fuzzy c-means clustering algorithm (called GMRFCM), which can significantly reduce the iterative times required to converge, the sensitivity to the initialization, and can obtain a better partition of a data set into c classes....

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Veröffentlicht in:	Proceedings of 2004 International Conference on Machine Learning and Cybernetics : August 6-29, 2004, Worldfield Convention Hotel, Shanghai, China Jg. 4; S. 2069 - 2073 vol.4
Hauptverfasser:	Yun-Ying Dong, Yun-Jie Zhang, Chun-Ling Chang
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 2004
Schlagworte:	Biological cells Clustering algorithms Convergence Fuzzy sets Genetic mutations Iterative algorithms Mathematics Partitioning algorithms Physics Sampling methods
ISBN:	0780384032, 9780780384033
Online-Zugang:	Volltext
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