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k-Means and k-Medoids Clustering
Cluster by minimizing mean or medoid distance, 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.
Topics
Introduction to Cluster Analysis
Understand the basic types of cluster analysis.
Partition data into k mutually exclusive clusters.
