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An efficient robust automatic clustering algorithm for interval data

Authors: 

Tai Vo-Van, Lethikimngoc, Thao Nguyen-Trang

Source title: 
Communications in Statistics - Simulation and Computation, 2021 (ISI)
Academic year of acceptance: 
2021-2022
Abstract: 

In recent years, clustering analysis for interval data has attracted the attention of many researchers. Nevertheless, an algorithm that can automatically determine the number of clusters, and can effectively detect the outlier intervals at the same time has not been studied so far. Therefore, in this paper, we propose a robust automatic clustering algorithm that only can automatically determine the number of clusters but also can assign the outlier intervals into separated clusters. The proposed algorithm is then applied in detecting the abnormal images consisting of the new image categories, and the images contaminated with noise.