In data mining and machine learning fields, k-means++ is an algorithm for choosing the initial values/centroids (or "seeds") for the k-means clustering algorithm. 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.
In data mining and machine learning fields, k-means++ is an algorithm for choosing the initial values/centroids (or "seeds") for the k-means clustering algorithm. 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.
Source: Wikipedia "K-means++" · CC BY-SA 4.0
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