k-means++ is an algorithm for choosing the initial values for k-means clustering in statistics and machine learning. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii as an approximation algorithm for the NP-hard k-means problem---a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm.
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- k-means++ is an algorithm for choosing the initial values for k-means clustering in statistics and machine learning. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii as an approximation algorithm for the NP-hard k-means problem---a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm.
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- k-means++ is an algorithm for choosing the initial values for k-means clustering in statistics and machine learning. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii as an approximation algorithm for the NP-hard k-means problem---a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm.
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