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Benjamin Moseley
Researcher at Carnegie Mellon University
Publications - 181
Citations - 4145
Benjamin Moseley is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Scheduling (computing) & Online algorithm. The author has an hindex of 24, co-authored 168 publications receiving 3465 citations. Previous affiliations of Benjamin Moseley include University of Illinois at Urbana–Champaign & Toyota Technological Institute.
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Journal ArticleDOI
Scalable k-means++
TL;DR: In this article, the authors show how to reduce the number of passes needed to obtain, in parallel, a good initialization of k-means++ in both sequential and parallel settings.
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Scalable K-Means++
TL;DR: It is proved that the proposed initialization algorithm k-means|| obtains a nearly optimal solution after a logarithmic number of passes, and Experimental evaluation on real-world large-scale data demonstrates that k-Means|| outperforms k- means++ in both sequential and parallel settings.
Proceedings ArticleDOI
Filtering: a method for solving graph problems in MapReduce
TL;DR: This paper presents new algorithms in the MapReduce framework for a variety of fundamental graph problems for sufficiently dense graphs and implements the maximal matching algorithm that lies at the core of the analysis and achieves a significant speedup over the sequential version.
Proceedings ArticleDOI
Fast clustering using MapReduce
TL;DR: In this article, the authors consider the problem of k-center and k-median clustering in MapReduce and develop fast clustering algorithms with constant factor approximation guarantees.
Journal ArticleDOI
Fast Greedy Algorithms in MapReduce and Streaming
TL;DR: A powerful sampling technique that aids in parallelization of sequential algorithms and yields efficient algorithms that run in a logarithmic number of rounds while obtaining solutions that are arbitrarily close to those produced by the standard sequential greedy algorithm.