Fuzzy c-ordered medoids clustering for interval-valued data

PATTERN RECOGNITION

卷:58

Article

D'Urso, Pierpaolo¹  Leski, Jacek M.^2,3 

[1] Univ Roma La Sapienza, Dept Social Sci & Econ, Ple Aldo Moro 5, Rome, Italy

[2] Silesian Tech Univ, Inst Elect, Akad 16, PL-44100 Gliwice, Poland

[3] Inst Med Technol & Equipment, Dept Comp Med Syst, Roosevelt St 118, PL-41800 Zabrze, Poland

关键词: Interval-valued data;    Outlier interval data;    Fuzzy c-ordered medoids clustering;    Huber's M-estimators;    Ordered weighted averaging;    Robust clustering;   

DOI  :  10.1016/j.patcog.2016.04.005

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier Ltd. All rights reserved.

【 授权许可】

Free