An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Uncertainty, fuzzy logic, and signal processing
Signal Processing - Special issue on fuzzy logic in signal processing
Centroid of a type-2 fuzzy set
Information Sciences: an International Journal
Enhanced Karnik-Mendel algorithms
IEEE Transactions on Fuzzy Systems
Belief C-Means: An extension of Fuzzy C-Means algorithm in belief functions framework
Pattern Recognition Letters
Information Sciences: an International Journal
Type-2 fuzzy sets and systems: an overview
IEEE Computational Intelligence Magazine
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to C-Means
IEEE Transactions on Fuzzy Systems
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Uncertainties are common in the applications like pattern recognition, image processing, etc., while FCM algorithm is widely employed in such applications. However, FCM is not quite efficient to handle the uncertainties well. Interval type-2 fuzzy theory has been incorporated into FCM to improve the ability for handling uncertainties of these algorithms, but the complexity of algorithm will increase accordingly. In this paper an enhanced interval type-2 FCM algorithm is proposed in order to reduce these shortfalls. The initialization of cluster center and the process of type-reduction are optimized in this algorithm, which greatly reduce the calculation time of interval type-2 FCM and accelerate the convergence of the algorithm. Many simulations have been performed on random data clustering and image segmentation to show the validity of our proposed algorithm.