Python, Machine Learning, Clustering, K Means, Sklearn.
number of clusters. Algorithm Input: K and points x1,x2,…,xn Place centroids at random c1,c2,…,ck locations Repeat until convergence -for each point xi calculate distance and find the closest centroid cj, argmin D (xi,cj), i.e. Euclidian. label the point xi as cluster cj -for each cluster j = 1…K;