Also known as: Centroid based Clustering
Centroid-based clustering is a family of clustering methods that partitions data into nonhierarchical groups around representative centers. K-means is a common example, assigning each data point to the cluster with the nearest centroid.
A category of clustering algorithms that organizes data into nonhierarchical clusters. k-means is the most widely used centroid-based clustering algorithm. Contrast with hierarchical clustering algorithms. See Clustering algorithms in the Clustering course for more information.