Encyclopedia of Machine Learning and Data Mining

2017 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

K-Medoids Clustering

  • Xin JinEmail author
  • Jiawei Han
Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7687-1_432


K-Medoids Clustering is a clustering method more robust to outliers than K-Means. Representative algorithms include Partitioning Around Medoids (PAM), CLARA, CLARANS, etc.

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Recommended Reading

  1. Chu S-C, Roddick JF, Pan J-S (2008) Improved search strategies and extensions to k-medoids-based clustering algorithms. Int J Bus Intell Data Min 3(2):212–231CrossRefGoogle Scholar
  2. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann Publishers, San FranciscozbMATHGoogle Scholar
  3. Kaufman L, Rousseeuw PJ (2005) Finding groups in data: an introduction to cluster analysis. Wiley series in probability and statistics. Wiley-Interscience, New YorkGoogle Scholar
  4. Ng RT, Han J (2002) CLARANS: a method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14(5):1003–1016CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.PayPal Inc.San JoseUSA
  2. 2.University of Illinios at Urbana-ChampaignUrbanaUSA