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The problem is computationally difficult ([[NP-hard]]); however, there are efficient [[heuristic algorithm]]s that are commonly employed and converge quickly to a local optimum. These are usually similar to the [[expectation-maximization algorithm]] for [[Mixture model|mixtures]] of [[Gaussian distribution]]s via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, ''k''-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.
The algorithm has nothing to do with and has not to be confused with [[k-nearest neighbor|''k''-nearest neighbor]], another popular [[machine learning]] technique.
== Description ==
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