  In this paper a fuzzy clustering model for fuzzy data with outliers is
proposed. 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. We have also defined a
transformation to change our 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.
