k-Means and k-Medoids Clustering
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 algorithm in the Live Editor|
Partition data into k mutually exclusive clusters.