A robust automatic clustering algorithm for probability density functions with application to categorizing color images. Issue 7 (9th August 2018)

• Record Type: Journal Article
• Authors: Chen, J. H.; Chang, Y. C.; Hung, W. L.
• Is Part Of: Communications in statistics. Volume 47:Issue 7(2018)
• Page Start: 2152
• Page End: 2168
• Publication Date: 2018-08-09
• Subjects: Clustering algorithms -- COREL image database -- Kernel density method -- Probability density function; 62H30 -- 6207; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2017.1337137
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: This study develops a robust automatic algorithm for clustering probability density functions based on the previous research. Unlike other existing methods that often pre-determine the number of clusters, this method can self-organize data groups based on the original data structure. The proposed clustering method is also robust in regards to noise. Three examples of synthetic data and a real-world COREL dataset are utilized to illustrate the accurateness and effectiveness of the proposed approach.