Praveen Kundurthy and Bob Chesebrough from Intel cover K-Means and Gpairs as examples to demonstrate the implementation of these algorithms with live sample code on the Intel DevCloud and/or JLSE. K-means is a clustering algorithm that partitions observations from a dataset into a requested number of geometric clusters of points closest to the cluster’s center of mass. Using an initial estimate of the centroids, the algorithm iteratively updates the positions of the centroids until a fixed point. Intel Extension for Scikit-learn* provides an optimized K-Means clustering algorithm.