| Document Date: 2013-08-11 21:51:56
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City Waterloo / / / Facility University of Illinois / / IndustryTerm m-median solution / local search algorithm / chosen algorithm / post processing technique leads / c-approximate solution / fault-tolerant k-median algorithm / m-median clustering approximation algorithm / fault-tolerant clustering algorithm / recent algorithm / post-processing technique / approximation algorithm / non-fault-tolerant mmedian algorithm / c-approximate m-center solution / local search / constant factor approximation algorithms / c-approximation algorithm / Local search heuristic / above algorithm / fault-tolerant solution / m-center solution / near optimal fault-tolerant solution / factor c-approximation algorithm / 2approximation algorithm / chosen approximation algorithm / optimum solution / non-fault-tolerant solution / m-center algorithm / / Organization University of Illinois / Federal Trade Commission / / Person Benjamin Raichel / / ProvinceOrState Illinois / Ontario / / Technology local search algorithm / 4.1 3.1 The algorithm / above algorithm / 2013 3 Algorithms Our algorithms / C. This algorithm / Geometric Approximation Algorithms / 12-approximation algorithm / 9-approximation algorithm / c-approximation algorithm / fault-tolerant k-median algorithm / chosen algorithm / approximation algorithm / chosen approximation algorithm / 4-approximation algorithm / 2-approximation algorithm / 2approximation algorithm / m-center algorithm / m-median clustering approximation algorithm / recent algorithm / fault-tolerant clustering algorithm / fault-tolerant k-median Fault-tolerant algorithms / non-fault-tolerant mmedian algorithm / constant factor approximation algorithms / factor c-approximation algorithm / /
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CCCG 2013, Waterloo, Ontario, August 8–10, 2013 Fault Tolerant Clustering Revisited Nirman Kumar∗ Abstract