k-Means andk-Medoids Clustering

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

k-meansandk-medoidsclusteringpartitions data intoknumber 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 distanceis 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 usingk-means algorithm in the Live Editor

Functions

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

Topics

k-Means Clustering
Partition data intokmutually exclusive clusters.