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Enhanced interval type-2 fuzzy c-means algorithm with improved initial center
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AI
DOI：10.1016/j.patrec.2013.11.011.png)
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Abstract
En 中文
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. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Fuzzy clustering
Fuzzy c-means
Interval type-2 fuzzy set
Type-reduction
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