A K-Means++ Clustering Implementation for VTK

Abstract

K-Means clustering is an excellent technique for clustering points when the number of clusters is known. We present a implementation (vtkKMeanClustering) of the algorithm written in a VTK context. We also implement the K-Means++ initialization method which finds the global optimum much more frequently than a naive/random initialization. The code is currently hosted at http://github.com/daviddoria/KMeansClustering .

No source code files available for this publication.