Fair k-Center Problem with Outliers on Massive Data  

作　　者：Fan Yuan Luhong Diao Donglei Du Lei Liu 

机构地区：[1]Beijing Institute for Scientific and Engineering Computing,Faculty of Science,Beijing University of Technology,Beijing 100124,China [2]Faculty of Management,University of New Brunswick,Fredericton,E3B 5A3,Canada

出　　处：《Tsinghua Science and Technology》2023年第6期1072-1084,共13页清华大学学报（自然科学版（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(Nos.12131003,11771386,and 11728104);the Beijing Natural Science Foundadtion Project(No.Z200002);the General Research Projects of Beijing Educations Committee in China(No.KM201910005013);the Natural Sciences and Engineering Research Council of Canada(NSERC)(No.06446);the General Program of Science and Technology Development Project of Beijing Municipal Education Commission(No.KM201810005005).

摘　　要：The clustering problem of big data in the era of artificial intelligence has been widely studied.Because of the huge amount of data,distributed algorithms are often used to deal with big data problems.The distributed computing model has an attractive feature:it can handle massive datasets that cannot be put into the main memory.On the other hand,since many decisions are made automatically by machines in today’s society,algorithm fairness is also an important research area of machine learning.In this paper,we study two fair clustering problems:the centralized fair k-center problem with outliers and the distributed fair k-center problem with outliers.For these two problems,we have designed corresponding constant approximation ratio algorithms.The theoretical proof and analysis of the approximation ratio,and the running space of the algorithm are given.

关 键 词：machine learning distributed algorithm fairness constraints outlier constraints k-center problem 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]