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Pattern Classification (2nd Edition)
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SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Improved smoothed analysis of the k-means method
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The Planar k-Means Problem is NP-Hard
WALCOM '09 Proceedings of the 3rd International Workshop on Algorithms and Computation
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Smoothed analysis: an attempt to explain the behavior of algorithms in practice
Communications of the ACM - A View of Parallel Computing
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APPROX '09 / RANDOM '09 Proceedings of the 12th International Workshop and 13th International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
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ICVS'08 Proceedings of the 6th international conference on Computer vision systems
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CEA'10 Proceedings of the 4th WSEAS international conference on Computer engineering and applications
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Proceedings of the 24th ACM International Conference on Supercomputing
Information Systems Frontiers
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Smoothed Analysis of the k-Means Method
Journal of the ACM (JACM)
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Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Proceedings of the VLDB Endowment
The planar k-means problem is NP-hard
Theoretical Computer Science
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Information Sciences: an International Journal
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Journal of the ACM (JACM)
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Pattern Recognition
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USENIX ATC'13 Proceedings of the 2013 USENIX conference on Annual Technical Conference
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The Journal of Machine Learning Research
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The k-means method is an old but popular clustering algorithm known for its observed speed and its simplicity. Until recently, however, no meaningful theoretical bounds were known on its running time. In this paper, we demonstrate that the worst-case running time of k-means is superpolynomial by improving the best known lower bound from Ω(n) iterations to 2Ω(√n).