On a class of fuzzy c-numbers clustering procedures for fuzzy data
Fuzzy Sets and Systems
Fuzzy clustering procedures for conical fuzzy vector data
Fuzzy Sets and Systems
Comparison of fuzzy numbers using a fuzzy distance measure
Fuzzy Sets and Systems - Fuzzy intervals
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Dynamic clustering of interval data using a Wasserstein-based distance
Pattern Recognition Letters
A weighted fuzzy c-means clustering model for fuzzy data
Computational Statistics & Data Analysis
A parametric model for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
Two nonparametric models for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
Computational Intelligence and Neuroscience
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This paper proposes a fuzzy clustering model for fuzzy data with outliers. The model is based on Wasserstein distance between interval valued data, which is generalized to fuzzy data. In addition, Keller's approach is used to identify outliers and reduce their influences. The authors also define a transformation to change the distance to the Euclidean distance. With the help of this approach, the problem of fuzzy clustering of fuzzy data is reduced to fuzzy clustering of crisp data. In order to show the performance of the proposed clustering algorithm, two simulation experiments are discussed.