K-Means++ Algorithm
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A K-Means++ Algorithm is a preprocessing algorithm to prepare a k-means clustering algorithm.
- See: NP-Hard.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/K-means++ Retrieved:2015-1-16.
- In data mining, k-means++ [1] is an algorithm for choosing the initial values (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. It is similar to the first of three seeding methods proposed, in independent work, in 2006 by Rafail Ostrovsky, Yuval Rabani, Leonard Schulman and Chaitanya Swamy. (The distribution of the first seed is different.)
- ↑ http://theory.stanford.edu/~sergei/slides/BATS-Means.pdf Slides for presentation of method by Arthur, D. and Vassilvitskii, S.