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k-means Clustering
A Processing visualization of the k-means clustering algorithm, demonstrating iterative centroid-based data partitioning and Voronoi cell formation for different values of k.
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
GitHub repository: https://github.com/Trimad/k-means-clustering