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Preclustering Algorithms for Imprecise Points

Authors Mohammad Ali Abam, Mark de Berg , Sina Farahzad, Mir Omid Haji Mirsadeghi, Morteza Saghafian



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Author Details

Mohammad Ali Abam
  • Department of Computer Engineering, Sharif University of Technology, Iran
Mark de Berg
  • Department of Mathematics and Computer Science, TU Eindhoven, the Netherlands
Sina Farahzad
  • Department of Computer Engineering, Sharif University of Technology, Iran
Mir Omid Haji Mirsadeghi
  • Department of Mathematical Sciences, Sharif University of Technology, Iran
Morteza Saghafian
  • Department of Mathematical Sciences, Sharif University of Technology, Iran

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Mohammad Ali Abam, Mark de Berg, Sina Farahzad, Mir Omid Haji Mirsadeghi, and Morteza Saghafian. Preclustering Algorithms for Imprecise Points. In 17th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 162, pp. 3:1-3:12, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.SWAT.2020.3

Abstract

We study the problem of preclustering a set B of imprecise points in ℝ^d: we wish to cluster the regions specifying the potential locations of the points such that, no matter where the points are located within their regions, the resulting clustering approximates the optimal clustering for those locations. We consider k-center, k-median, and k-means clustering, and obtain the following results. Let B:={b₁,…,b_n} be a collection of disjoint balls in ℝ^d, where each ball b_i specifies the possible locations of an input point p_i. A partition 𝒞 of B into subsets is called an (f(k),α)-preclustering (with respect to the specific k-clustering variant under consideration) if (i) 𝒞 consists of f(k) preclusters, and (ii) for any realization P of the points p_i inside their respective balls, the cost of the clustering on P induced by 𝒞 is at most α times the cost of an optimal k-clustering on P. We call f(k) the size of the preclustering and we call α its approximation ratio. We prove that, even in ℝ^1, one may need at least 3k-3 preclusters to obtain a bounded approximation ratio - this holds for the k-center, the k-median, and the k-means problem - and we present a (3k,1) preclustering for the k-center problem in ℝ^1. We also present various preclusterings for balls in ℝ^d with d⩾2, including a (3k,α)-preclustering with α≈13.9 for the k-center and the k-median problem, and α≈254.7 for the k-means problem.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • Geometric clustering
  • k-center
  • k-means
  • k-median
  • imprecise points
  • approximation algorithms

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References

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