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MapReduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension

2017
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Proceedings of the VLDB Endowment
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Given a dataset of points in a metric space and an integer k, a diversity maximization problem requires determining a subset of k points maximizing some diversity objective measure, e.g., the minimum or the average distance between two points in the subset. Diversity maximization is computationally hard, hence only approximate solutions can be hoped for. Although its applications are mainly in massive data analysis, most of the past research on diversity maximization focused on the sequential

doi:10.14778/3055540.3055541
fatcat:2w6lms7vi5hs7ekiivf757w4pi