Fuzzy c-ordered medoids clustering for interval-valued data

作者：

Highlights：

• 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.

摘要

Highlights•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.

论文关键词：Interval-valued data,Outlier interval data,Fuzzy c-ordered medoids clustering,Huber׳s M-estimators,Ordered weighted averaging,Robust clustering

论文评审过程：Received 1 April 2015, Revised 22 March 2016, Accepted 9 April 2016, Available online 16 April 2016, Version of Record 26 May 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.04.005