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 Data

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

Functions

`kmeans`	k-means clustering
`kmedoids`	k-medoids clustering
`mahal`	Mahalanobis distance to reference samples

Topics

k-Means Clustering
Partition data into k mutually exclusive clusters.