A jackknife entropy-based clustering algorithm for probability density functions

被引：3

作者：

Chen, Jen-Hao ^{[1
]}

Hung, Wen-Liang ^{[2
]}

机构：

[1] Natl Tsing Hua Univ, Inst Computat & Modeling Sci, Hsinchu, Taiwan

[2] Natl Tsing Hua Univ, Ctr Teacher Educ, Hsinchu, Taiwan

来源：

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2021年 / 91卷 / 05期

关键词：

Cluster analysis; entropy; jackknife; probability density function; variance ratio criterion;

D O I：

10.1080/00949655.2020.1832490

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：861 / 875

页数：15

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