K-means and gaussian mixture modeling with a separation constraint

被引：0

作者：

Jiang, He ^{[1
]}

Arias-Castro, Ery ^{[2
]}

机构：

[1] Calif State Polytech Univ Pomona, Dept Math & Stat, Pomona, CA 91768 USA

[2] Univ Calif San Diego, Dept Math, La Jolla, CA USA

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2024年

基金：

美国国家科学基金会;

关键词：

Clustering; Dynamic programming; Gaussian mixture models; K-means; Separation constraint; MAXIMUM-LIKELIHOOD-ESTIMATION; RESTRICTED EM ALGORITHM;

D O I：

10.1080/03610918.2024.2354747

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：

We consider the problem of clustering with K-means and Gaussian mixture models with a constraint on the separation between the centers in the context of real-valued data. We first propose a dynamic programming approach to solving the K-means problem with a separation constraint on the centers, building on Wang and Song (2011). In the context of fitting a Gaussian mixture model, we then propose an EM algorithm that incorporates such a constraint. A separation constraint can help regularize the output of a clustering algorithm, and we provide both simulated and real data examples to illustrate this point.

引用

页数：15

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