Improved Guarantees for k-means plus plus and k-means plus plus Parallel

被引：0

作者：

Makarychev, Konstantin ^{[1
]}

Reddy, Aravind ^{[1
]}

Shan, Liren ^{[1
]}

机构：

[1] Northwestern Univ, Dept Comp Sci, Evanston, IL 60208 USA

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020 | 2020年 / 33卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we study k-means++ and k-means parallel to, the two most popular algorithms for the classic k-means clustering problem. We provide novel analyses and show improved approximation and bi-criteria approximation guarantees for k-means++ and k-means parallel to. Our results give a better theoretical justification for why these algorithms perform extremely well in practice.

引用

页数：11

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