Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

K-Medoids Clustering

  • Xin Jin
  • Jiawei Han
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_426
The K-means clustering algorithm is sensitive to outliers, because a mean is easily influenced by extreme values. K-medoids clustering is a variant of K-means that is more robust to noises and outliers. Instead of using the mean point as the center of a cluster, K-medoids uses an actual point in the cluster to represent it. Medoid is the most centrally located object of the cluster, with minimum sum of distances to other points. Figure  1 shows the difference between mean and medoid in a 2-D example. The group of points in the right form a cluster, while the rightmost point is an outlier. Mean is greatly influenced by the outlier and thus cannot represent the correct cluster center, while medoid is robust to the outlier and correctly represents the cluster center.
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Recommended Reading

  1. Kaufman, L., & Rousseeuw, P. J. (2005). Finding groups in data: An introduction to cluster analysis (Wiley series in probability and statistics). New York: Wiley-Interscience.Google Scholar
  2. Ng, R. T., & Han, J. (2002). Clarans: A method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering, 14(5), 1003–1016.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Xin Jin
  • Jiawei Han

There are no affiliations available