1. Overview K-means clustering is a simple and elegant approach for partitioning a data set into K distinct, nonoverlapping clusters. To perform K-means clustering , we must first specify the desired number of clusters K ; then, the K-means algorithm will assign each observation to exactly one of the K clusters. The below figure shows the results obtained from performing K-means clustering on a simulated example consisting of 150 observations in two dimensions, using three different values of K . KMeans # class sklearn.cluster.KMeans (n_clusters=8, ", init=' k -means++', n_init='auto', max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='lloyd') [source] # K-Means clustering . Read more in the User Guide. Parameters: n_clustersint, default=8 The number of clusters to form as well as the number of centroids to generate. For an example of how to choose an optimal value for n_clusters refer to Selecting the number of clusters with silhouette analysis on KMeans ... What is k-means clustering? K-means clustering is an unsupervised learning algorithm used for data clustering, which groups unlabeled data points into groups or clusters. It is one of the most popular clustering methods used in machine learning. k-means clustering is a method of vector quantization , originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid).
