Center-based clustering is a fundamental primitive for data analysis and is very challenging for large datasets. We developed coreset based space/round-efficient MapReduce algorithms to solve the k-center, k-median, and k-means variants in general metrics. Remarkably, the algorithms obliviously adapt to the doubling dimension of the metric space, and attain approximation ratios that can be made arbitrarily close to those achievable by the best known polynomial-time sequential approximations.
Distributed Clustering in General Metrics via Coresets
Mazzetto, Alessio
2019/2020
Abstract
Center-based clustering is a fundamental primitive for data analysis and is very challenging for large datasets. We developed coreset based space/round-efficient MapReduce algorithms to solve the k-center, k-median, and k-means variants in general metrics. Remarkably, the algorithms obliviously adapt to the doubling dimension of the metric space, and attain approximation ratios that can be made arbitrarily close to those achievable by the best known polynomial-time sequential approximations.File in questo prodotto:
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Utilizza questo identificativo per citare o creare un link a questo documento:
https://hdl.handle.net/20.500.12608/28042