Clustering Algorithms: a Review

  • Boris Mirkin
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 11)


  • A review of clustering concepts and algorithms is provided emphasizing: (a) output cluster structure, (b) input data kind, and (c) criterion.

  • A dozen cluster structures is considered including those used in either supervised or unsupervised learning or both.

  • The techniques discussed cover such algorithms as nearest neighbor, K-Means (moving centers), agglomerative clustering, conceptual clustering, EM-algorithm, high-density clustering, and back-propagation.

  • Interpretation is considered as achieving clustering goals (partly, via presentation of the same data with both extensional and intensional forms of cluster structures).


Cluster Technique Cluster Structure Classification Structure Local Search Algorithm Average Similarity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Boris Mirkin
    • 1
  1. 1.DIMACSRutgers UniversityUSA

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