Demonstrates superpolynomial running time bounds for the k-means clustering algorithm, indicating its computational limits.
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).
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Arthur et al. (2006) studied this question.
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