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Fast and Accurate Fair k-Center Clustering in Doubling Metrics
WWW '24: Proceedings of the ACM Web Conference 2024Pages 756–767https://doi.org/10.1145/3589334.3645568We study the classic k-center clustering problem under the additional constraint that each cluster should be fair. In this setting, each point is marked with one or more colors, which can be used to model protected attributes (e.g., gender or ethnicity). ...
- research-articleAugust 2020
A General Coreset-Based Approach to Diversity Maximization under Matroid Constraints
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 5Article No.: 60, Pages 1–27https://doi.org/10.1145/3402448Diversity maximization is a fundamental problem in web search and data mining. For a given dataset S of n elements, the problem requires to determine a subset of S containing k≪n “representatives” which maximize some diversity function expressed in ...
- research-articleFebruary 2018
Fast Coreset-based Diversity Maximization under Matroid Constraints
WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data MiningPages 81–89https://doi.org/10.1145/3159652.3159719Max-sum diversity is a fundamental primitive for web search and data mining. For a given set S of n elements, it returns a subset of k«l n representatives maximizing the sum of their pairwise distances, where distance models dissimilarity. An important ...
