This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

라이선스가 부여된 사용자만 번역 문서를 볼 수 있습니다. 번역 문서를 보려면 로그인하십시오.

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 measure computed using the mean and standard deviation of the sample data, and accounts for correlation within the data.


kmeansk-means clustering
kmedoidsk-medoids clustering
mahalMahalanobis distance


Introduction to Cluster Analysis

Understand the basic types of cluster analysis.

k-Means Clustering

Partition data into k mutually exclusive clusters.

Was this topic helpful?