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Finding a Cluster in Incomplete Data

Authors Eduard Eiben , Robert Ganian , Iyad Kanj , Sebastian Ordyniak , Stefan Szeider

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Author Details

Eduard Eiben
  • Department of Computer Science, Royal Holloway, University of London, Egham, UK
Robert Ganian
  • Algorithms and Complexity Group, TU Wien, Austria
Iyad Kanj
  • School of Computing, DePaul University, Chicago, IL, USA
Sebastian Ordyniak
  • University of Leeds, School of Computing, Leeds, UK
Stefan Szeider
  • Algorithms and Complexity Group, TU Wien, Austria

Funding

  • Ganian, Robert: Project No. Y1329 of the Austrian Science Fund (FWF).
  • Ordyniak, Sebastian: Project EP/V00252X/1 of the Engineering and Physical Sciences Research Council (EPSRC).
  • Szeider, Stefan: Project No. P32441 of the Austrian Science Fund (FWF) and Project No. ICT19-065 of the Vienna Science and Technology Fund (WWTF).

Cite As Get BibTex

Eduard Eiben, Robert Ganian, Iyad Kanj, Sebastian Ordyniak, and Stefan Szeider. Finding a Cluster in Incomplete Data. In 30th Annual European Symposium on Algorithms (ESA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 244, pp. 47:1-47:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.ESA.2022.47

Abstract

We study two variants of the fundamental problem of finding a cluster in incomplete data. In the problems under consideration, we are given a multiset of incomplete d-dimensional vectors over the binary domain and integers k and r, and the goal is to complete the missing vector entries so that the multiset of complete vectors either contains (i) a cluster of k vectors of radius at most r, or (ii) a cluster of k vectors of diameter at most r. We give tight characterizations of the parameterized complexity of the problems under consideration with respect to the parameters k, r, and a third parameter that captures the missing vector entries.

Subject Classification

ACM Subject Classification
  • Theory of computation → Parameterized complexity and exact algorithms
Keywords
  • Parameterized complexity
  • incomplete data
  • clustering

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