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 ...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.展开更多
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)+2 种基金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).
文摘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.
