A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Modeling for Control
The Journal of Machine Learning Research
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Supervised fuzzy clustering for rule extraction
IEEE Transactions on Fuzzy Systems
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
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In order to overcome the dimension problem of the traditional fuzzy clustering, we use kernel-based fuzzy c-means clustering (KFCM) to construct first-order TSK fuzzy models. The proposed algorithm is composed of two phases. In the first phase, the antecedent fuzzy sets are obtained by KFCM. We present the expression of the cluster prototypes of KFCM with different kernel functions in original input space. The use of cluster validity indices is a standard approach to determine an appropriate number of clusters in a data set. However, cluster validity index demands running the clustering algorithm for different number of clusters repeatedly. Therefore, a novel method specifying the number of clusters automatically is given for the purpose of reducing the computational complexity and eliminating the outliers. In the second phase, the consequent parameters can be identified by the least squares method. Experiment results show that the proposed method improves the generalization ability and robustness of fuzzy models compared with the traditional techniques.