A fast consistent grid-based clustering algorithm

被引：0

作者：

Anton S. Tarasenko ^{[1
]}

Vladimir B. Berikov ^{[2
]}

Igor A. Pestunov ^{[1
]}

Sergey A. Rylov ^{[2
]}

Pavel S. Ruzankin ^{[3
]}

机构：

[1] Sobolev Institute of Mathematics,

[2] Novosibirsk State University,undefined

[3] Federal Research Center for Information and Computational Technologies,undefined

来源：

Pattern Analysis and Applications | 2024年 / 27卷 / 4期

关键词：

Clustering; Estimator for the number of clusters; Density level sets; Big data;

D O I：

10.1007/s10044-024-01354-0

中图分类号：

学科分类号：

摘要：

We propose a fast consistent grid-based algorithm that estimates the number of clusters for observations in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\mathbb {R}}}^d$$\end{document} and, besides, constructs an approximation for the clusters. Consistency is proved under certain conditions. The time complexity of the algorithm can be made linear retaining the consistency. Numerical experiments confirm high computational efficiency of the new algorithm and its ability to process large datasets.

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