K-means clustering

The K-means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster. The k-means algorithm is one of the fastest clustering algorithms available. K-means can group data only unsupervised based on the similarity of customers to each other. It is a type of partitioning clustering, as it divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels. The objective of k-means is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.