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, 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.