Distributed Fair k-Center Clustering Problems with Outliers

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,...

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Bibliographic Details
Published inParallel and Distributed Computing, Applications and Technologies pp. 430 - 440
Main Authors Yuan, Fan, Diao, Luhong, Du, Donglei, Liu, Lei
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Approximate algorithm
center problem with outliers
Clustering problem
Distributed fair
Fair
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Summary: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.
ISBN:9783030967710
3030967719
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-96772-7_39