A jackknife entropy-based clustering algorithm for probability density functions. Issue 5 (24th March 2021)

• Record Type: Journal Article
• Authors: Chen, Jen-Hao; Hung, Wen-Liang
• Is Part Of: Journal of statistical computation and simulation. Volume 91:Issue 5(2021)
• Page Start: 861
• Page End: 875
• Publication Date: 2021-03-24
• Subjects: Cluster analysis -- entropy -- jackknife -- probability density function -- variance ratio criterion; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1832490
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : This paper proposes a new unsupervised learning algorithm called jackknife entropy-based clustering algorithm for grouping families of probability density functions (pdfs). The fitness function is used to choose the best threshold values of similarity in the proposed algorithm. We demonstrate the correctness and robustness of the proposed algorithm on a synthetic data set. Finally, we apply the algorithm to texture clustering.