K-means [6] is a classical machine learning algorithm for clustering. It is intuitively easy to understand. Based on a predetermined number of clusters the user decides, it attempts to create clusters. This is done by reducing the distance of points from the respective centroid the point is assigned to. It is an iterative algorithm and keeps doing the process until the centroids and points assigned don't change. It is worth one's time to go through the theory behind the algorithm, though it isn't necessary for us to proceed.
Using K-means with scikit-learn is very easy, and scikit-learn offers two implementations [7] which we can use – either in mini-batches or without. In our code, we allow the user to toggle between which option to use:
minibatch = True
if minibatch: km = MiniBatchKMeans(n_clusters=true_k, init='k-means++', n_init...
