Distributed Fair k-Center Clustering Problems with Outliers

被引：2

作者：

Yuan, Fan ^{[1
]}

Diao, Luhong ^{[1
]}

Du, Donglei ^{[2
]}

Liu, Lei ^{[1
]}

机构：

[1] Beijing Univ Technol, Dept Operat Res & Informat Engn, Beijing 100124, Peoples R China

[2] Univ New Brunswick, Fac Management, Fredericton, NB E3B 5A3, Canada

来源：

PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES, PDCAT 2021 | 2022年 / 13148卷

基金：

北京市自然科学基金; 加拿大自然科学与工程研究理事会; 中国国家自然科学基金;

关键词：

Clustering problem; Approximate algorithm; Fair k-center problem with outliers; Distributed fair k-center problem with outliers;

D O I：

10.1007/978-3-030-96772-7_39

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Big data clustering is a fundamental problem with a vast number of applications. Due to the increasing size of data, interests in clustering problems in distributed computation models have increased. On the other hand, because important decision making is being automated with the help of algorithms, therefore, fairness in algorithms has become an especially important research topic. In this work, we design new distributed algorithms for the fair k-center problem with outliers. Our main contributions are: (1) In the fair k-center problem with outliers setting we give a 4-approximation ratio algorithm. (2) In the distributed fair k-center problem with outliers setting we give a 18-approximation ratio algorithm.

引用

页码：430 / 440

页数：11