Keywords

#k-means++

K-means++

Algorithm in data mining

In data mining, k-means++ is an algorithm for choosing the initial values 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.

Fri 23rd

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K-means clustering K-means++ Fuzzy clustering K-medoids Principal component analysis Feature learning Cluster analysis Mixture model Spectral clustering Nathan Netanyahu
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