Fuzzy c-ordered medoids clustering for interval-valued data

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. •Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data.•A new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID) is proposed•The method uses both the Huber׳s M-estimators and the Yager׳s OWA operators to obtain its robustness.•Experiments performed on synthetic data with different types of outliers and a real application are provided.