Smoothed Analysis of the k-Means Method
Journal of the ACM (JACM)
Proceedings of the VLDB Endowment
The effectiveness of lloyd-type methods for the k-means problem
Journal of the ACM (JACM)
Multi-robot, dynamic task allocation: a case study
Intelligent Service Robotics
Theoretical Computer Science
Scalable K-Means by ranked retrieval
Proceedings of the 7th ACM international conference on Web search and data mining
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The k-means algorithm is a well-known method for partitioning n points that lie in the d-dimensional space into k clusters. Its main features are simplicity and speed in practice. Theoretically, however, the best known upper bound on its running time (i.e., n O(kd)) is, in general, exponential in the number of points (when kd=Ω(n/log n)). Recently Arthur and Vassilvitskii (Proceedings of the 22nd Annual Symposium on Computational Geometry, pp. 144–153, 2006) showed a super-polynomial worst-case analysis, improving the best known lower bound from Ω(n) to $2^{\varOmega (\sqrt{n})}$ with a construction in $d=\varOmega (\sqrt{n})$ dimensions. In Arthur and Vassilvitskii (Proceedings of the 22nd Annual Symposium on Computational Geometry, pp. 144–153, 2006), they also conjectured the existence of super-polynomial lower bounds for any d≥2. Our contribution is twofold: we prove this conjecture and we improve the lower bound, by presenting a simple construction in the plane that leads to the exponential lower bound 2Ω(n).