K-medoids

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K-medoids is the name of a clustering algorithm related to the K-means algorithm. Both algorithms are partitional (breaking the dataset up into groups) and both attempt to minimize squared error, the distance between points labeled to be in a cluster and a point designated as the center of that cluster. In K-means, the point is 'artificial' — it is purely the average of all the points in a cluster. In K-medoids, the most centrally located object in the cluster is used, and thus the center is one of the actual datapoints.

[edit] See also

• Data clustering
• Linde-Buzo-Gray Algorithm