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A Bi-Criteria Approximation Algorithm for k-Means

We consider the classical k-means clustering problem in the setting of bi-criteria approximation, in which an algorithm is allowed to output beta*k > k clusters, and must produce a clustering with cost at most alpha times the to the cost of the optimal set of k clusters. We argue that this approach is natural in many settings, for which the exact number of clusters is a priori unknown, or unimportant up to a constant factor. We give new bi-criteria approximation algorithms, based on linear programming and local search, respectively, which attain a guarantee alpha(beta) depending on the number beta*k of clusters that may be opened. Our guarantee alpha(beta) is always at most 9 + epsilon and improves rapidly with beta (for example: alpha(2) < 2.59, and alpha(3) < 1.4). Moreover, our algorithms have only polynomial dependence on the dimension of the input data, and so are applicable in high-dimensional settings.

Editor(s):

Jansen, Klaus

Mathieu, Claire

Rolim, José D.P.

Umans, Chris

Published in:

Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2016), 14:1--14:20

Publisher:

Dagstuhl, Germany, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik

ISBN:

978-3-95977-018-7

Keywords:

Laboratories:

Record created 2017-08-03, last modified 2018-03-17