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Multi-attribute based fuzzy C-means in approximated feature space Conference paper
2017 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2017
Authors:  Liu Z.;  Chen C.L.P.;  Chen L.;  Zhou J.
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Fuzzy C-means  Maximum Entropy Method for weighted data  Quasi-Monte Carlo Feature maps  Random Features  Random Fourier Features maps  Weighted Multi-kernel Fuzzy C-means  
Multi-Attribute Based Fuzzy C-means in Approximated Feature Space Conference paper
Authors:  Liu, Zhulin;  Chen, C. L. Philip;  Chen, Long;  Zhoto, Jin;  IEEE
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Fuzzy C-means  Random Features  Random Fourier Features maps  Quasi-Monte Carlo Feature maps  Weighted Multi-kernel Fuzzy C-means  Maximum Entropy Method for weighted data  
Fuzzy clustering with the entropy of attribute weights Journal article
Neurocomputing, 2016,Volume: 198,Page: 125
Authors:  Zhou J.;  Chen L.;  Chen C.L.P.;  Zhang Y.;  Li H.-X.
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Attribute-weighted Clustering  Feature Selection  Kernel Method  Maximum-entropy Regularization  
A distributed K-means clustering algorithm in wireless sensor networks Conference paper
ICCSS 2015 - Proceedings: 2015 International Conference on Informative and Cybernetics for Computational Social Systems, Chengdu, PEOPLES R CHINA, AUG 13-15, 2015
Authors:  Zhou J.;  Zhang Y.;  Jiang Y.;  Chen C.L.P.;  Chen L.
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Attribute-weighted Clustering  Distributed Clustering  Wireless Sensor Networks  
Parallel attribute-weighted fuzzy c-means algorithm for clustering Conference paper
Proceedings 2012 International Conference on System Science and Engineering, ICSSE 2012, Dalian, Liaoning, China, 30 June-2 July 2012
Authors:  Zhou J.;  Chen C.L.P.;  Chen L.
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Attribute-weighted Clustering  Parallel Clustering  Wireless Sensor Network  
Attribute weight entropy regularization in fuzzy C-means algorithm for feature selection Conference paper
Proceedings 2011 International Conference on System Science and Engineering, ICSSE 2011
Authors:  Zhou J.;  Chen C.L.P.
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Attribute weight entropy regularization  Feature selection  Fuzzy c-means  Weighted fuzzy clustering