Cohen-Addad, V., Feldmann, A.E. orcid.org/0000-0001-6229-5332 and Saulpic, D. (2020) Near-linear time approximations schemes for clustering in doubling metrics. In: 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS). 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), 09-12 Nov 2019, Baltimore, MD, USA. Institute of Electrical and Electronics Engineers (IEEE) , pp. 540-559. ISBN 9781728149530
Abstract
We consider the classic Facility Location, k-Median, and k-Means problems in metric spaces of constant doubling dimension. We give the first nearly linear-time approximation schemes for each problem, making a significant improvement over the state-of-the-art algorithms. Moreover, we show how to extend the techniques used to get the first efficient approximation schemes for the problems of prize-collecting k-Medians and k-Means, and efficient bicriteria approximation schemes for k-Medians with outliers, k-Means with outliers and k-Center.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | clustering; k median; k means; approximation algorithms; PTAS |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 Jun 2023 11:10 |
Last Modified: | 28 Jun 2023 11:35 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/focs.2019.00041 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200955 |