Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
A deterministic annealing approach to clustering
Pattern Recognition Letters
On quantification of different facets of uncertainty
Fuzzy Sets and Systems
Entropy-based fuzzy clustering and fuzzy modeling
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Constrained Clustering as an Optimization Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A global optimization technique for statistical classifier design
IEEE Transactions on Signal Processing
Image thresholding using fuzzy entropies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effective fuzzy c-means clustering algorithms for data clustering problems
Expert Systems with Applications: An International Journal
Improved Fuzzy Clustering Algorithms in Segmentation of DC-enhanced breast MRI
Journal of Medical Systems
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This paper deals with statistical mechanical characteristics of fuzzy clustering regularized with fuzzy entropy. We obtain the Fermi-Dirac distribution function as a membership function by regularizing the fuzzy c-means with fuzzy entropy. Then we formulate it as a direct annealing clustering, and examine the meanings of Fermi-Dirac function and fuzzy entropy from a statistical mechanical point of view, and show that this fuzzy clustering method is none other than the Fermi-Dirac statistics.