k-means clustering

The following is an implementation of the k-means algorithm for educational purpose. This algorithm is widely known in the signal processing

The aim of the algorithm is to cluster n points (samples or observations) into k groups in which each point belongs to the cluster with the nearest mean. This process continues until there is no change in the clusters or the algorithm has reached the limit of iteration.
The algorithm has 3 values of interest: the number of points, k (number of clusters) and the number of iterations.
The code was designed in a way you can watch the movement of the cluster at each iteration.
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