A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
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Information Sciences—Applications: An International Journal
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EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
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Applied Soft Computing
A Fuzzy Cluster Algorithm Based on Mutative Scale Chaos Optimization
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study
Fuzzy Sets and Systems
Chaotic bee colony algorithms for global numerical optimization
Expert Systems with Applications: An International Journal
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Fuzzy Sets and Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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Clustering is a popular data analysis and data mining technique. In this paper, a novel chaotic particle swarm fuzzy clustering (CPSFC) algorithm based on chaotic particle swarm (CPSO) and gradient method is proposed. Fuzzy clustering model optimization is challenging, in order to solve this problem, adaptive inertia weight factor (AIWF) and iterative chaotic map with infinite collapses (ICMIC) are introduced, and a new CPSO algorithm combined AIWF and ICMIC based chaotic local search is studied. The CPSFC algorithm utilizes CPSO to search the fuzzy clustering model, exploiting the searching capability of fuzzy c-means (FCM) and avoiding its major limitation of getting stuck at locally optimal values. Meanwhile, gradient operator is adopted to accelerate convergence of the proposed algorithm. Its superiority over the FCM algorithm and another two global optimization algorithm-based clustering methods is extensively demonstrated for several artificial and real life data sets in comparative experiments.