K-means clustering

The K-means clustering algorithm, a commonly used clustering algorithm, is an iterative unsupervised learning 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. It aims to minimize the “intra cluster” distances and maximize the “inter-cluster” distances, and to divide the data into non-overlapping clusters without any cluster-internal structure.

See also

• Clustering
• Cluster