the ones and nines are somewhat split upbut most of the classes form single dense group keep in mind that this method has no knowledge of the class labelsit is completely unsupervised stillit can find representation of the data in two dimensions that clearly separates the classesbased solely on how close points are in the original space the -sne algorithm has some tuning parametersthough it often works well with the default settings you can try playing with perplexity and early_exaggerationbut the effects are usually minor clustering as we described earlierclustering is the task of partitioning the dataset into groupscalled clusters the goal is to split up the data in such way that points within single cluster are very similar and points in different clusters are different similarly to classification algorithmsclustering algorithms assign (or predicta number to each data pointindicating which cluster particular point belongs to -means clustering -means clustering is one of the simplest and most commonly used clustering algorithms it tries to find cluster centers that are representative of certain regions of the data the algorithm alternates between two stepsassigning each data point to the closest cluster centerand then setting each cluster center as the mean of the data points that are assigned to it the algorithm is finished when the assignment of instances to clusters no longer changes the following example (figure - illustrates the algorithm on synthetic datasetin[ ]mglearn plots plot_kmeans_algorithm( unsupervised learning and preprocessing