Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Upper and lower values for the level of fuzziness in FCM
Information Sciences: an International Journal
A cluster validity index for fuzzy clustering
Information Sciences: an International Journal
Active semi-supervised fuzzy clustering
Pattern Recognition
Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm
Information Sciences: an International Journal
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization
Information Sciences: an International Journal
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Classification and prediction of road traffic using application-specific fuzzy clustering
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
Algorithms of fuzzy clustering with partial supervision
Pattern Recognition Letters
A learning scheme for a fuzzy k-NN rule
Pattern Recognition Letters
Improving feature space based image segmentation via density modification
Information Sciences: an International Journal
Introducing the Discriminative Paraconsistent Machine (DPM)
Information Sciences: an International Journal
Information Sciences: an International Journal
Fuzzy partition based soft subspace clustering and its applications in high dimensional data
Information Sciences: an International Journal
Uncovering overlapping cluster structures via stochastic competitive learning
Information Sciences: an International Journal
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Fuzzy clustering is an effective clustering approach which associates a data point with multiple clusters. Standard fuzzy clustering models like fuzzy c-means are based on minimizing the total cluster variation, which is defined as the sum of the distances between the data points and their corresponding cluster centers weighted by the membership degrees. In this paper, we propose a fuzzy minimax clustering model by minimizing the maximum value of the set of weighted cluster variations in such a way that they satisfy a prior distribution. We derive a necessary condition for the extremum point of the fuzzy minimax clustering model, and then design an iterative algorithm for solving the extremum point. Several numerical examples on comparing fuzzy c-means and fuzzy minimax clustering models are given, which demonstrate that the prior distribution improves the quality of the clustering results significantly.