Eiben, E, Ganian, R, Kanj, I et al. (2 more authors) (2021) The Parameterized Complexity of Clustering Incomplete Data. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence: AAAI-21 Technical Tracks 8. Thirty-Fifth AAAI Conference on Artificial Intelligence, 02-09 Feb 2021, Virtual Event. , pp. 7296-7304. ISBN 978-1-57735-866-4
Abstract
We study fundamental clustering problems for incomplete data. Specifically, given a set of incomplete d-dimensional vectors (representing rows of a matrix), the goal is to complete the missing vector entries in a way that admits a partitioning of the vectors into at most k clusters with radius or diameter at most r. We give tight characterizations of the parameterized complexity of these problems with respect to the parameters k, r, and the minimum number of rows and columns needed to cover all the missing entries. We show that the considered problems are fixed-parameter tractable when parameterized by the three parameters combined, and that dropping any of the three parameters results in parameterized intractability. A byproduct of our results is that, for the complete data setting, all problems under consideration are fixed-parameter tractable parameterized by k+r.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Clustering |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/V00252X/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Jul 2021 15:07 |
Last Modified: | 06 Aug 2021 10:38 |
Published Version: | https://ojs.aaai.org/index.php/AAAI/article/view/1... |
Status: | Published |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176332 |