Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data. The
Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a
partitioning around medoids approach. However, one of the greatest disadvantages of this method is its
sensitivity to the presence of outliers in data. This paper introduces a new robust fuzzy clustering
method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID). The Huber's
M-estimators and the Yager's Ordered Weighted Averaging (OWA) operators are used in the method
proposed to make it robust to outliers. The described algorithm is compared with the fuzzy c-medoids
method in the experiments performed on synthetic data with different types of outliers. A real application of the FcOMdC-ID is also provided
