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k-Means and k-Medoids Clustering

Cluster by minimizing mean or medoid distance, and calculate Mahalanobis distance

k-means and k-medoids clustering partitions data into k number of mutually exclusive clusters. These techniques assign each observation to a cluster by minimizing the distance from the data point to the mean or median location of its assigned cluster, respectively. Mahalanobis distance is a unitless metric computed using the mean and standard deviation of the sample data, and accounts for correlation within the data.

Live Editor Tasks

Cluster DataCluster data using k-means or hierarchical clustering in the Live Editor (Since R2021b)


kmeansk-means clustering
kmedoidsk-medoids clustering
mahalMahalanobis distance to reference samples