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- W2950622591 abstract "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." @default.
- W2950622591 created "2019-06-27" @default.
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- W2950622591 date "2013-01-01" @default.
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- W2950622591 title "A Fuzzy Clustering Model for Fuzzy Data with Outliers" @default.
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- W2950622591 doi "https://doi.org/10.4018/978-1-4666-1870-1.ch003" @default.
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