A self-organizing clustering algorithm for functional data

被引：1

作者：

Chen, Jen-Hao ^{[1
]}

Chang, Yen-Chang ^{[2
]}

Hung, Wen-Liang ^{[3
,4
]}

机构：

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

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

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

[4] Natl Tsing Hua Univ, Dept Comp Sci, Hsinchu, Taiwan

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2020年 / 49卷 / 05期

关键词：

Clustering; k-means; Functional data; Self-organizing algorithm;

D O I：

10.1080/03610918.2018.1494280

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Based on a learning schema, we proposed a self-organizing clustering algorithm for functional data. The proposed algorithm can be used to cluster functional data without any prior knowledge of present clusters in the data set. The resulting clusters represent homogeneity in the input data. The theoretical analysis of the convergence of the proposed algorithm is also given. Comparisons with three well-known clustering algorithms, fuzzy k-means, k-means and Funclust, show that the proposed algorithm outperforms these competitors and provides better corrected classification rate and value of variance ratio criterion.

引用

页码：1237 / 1263

页数：27

共 25 条

[1] Unsupervised curve clustering using B-splines [J].