Journal of Classification

, Volume 24, Issue 1, pp 99–121 | Cite as

Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques

  • Douglas Steinley
  • Michael J. Brusco


K-means clustering is arguably the most popular technique for partitioning data. Unfortunately, K-means suffers from the well-known problem of locally optimal solutions. Furthermore, the final partition is dependent upon the initial configuration, making the choice of starting partitions all the more important. This paper evaluates 12 procedures proposed in the literature and provides recommendations for best practices.


Initial Seed Initialization Procedure Initialization Strategy Random Initialization Multivariate Behavioral Research 
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

© Springer Science + Business Media Inc. 2007

Authors and Affiliations

  • Douglas Steinley
    • 1
  • Michael J. Brusco
    • 2
  1. 1.University of Missouri-ColumbiaColumbia, MOUSA
  2. 2.University of FloridaGainesville, FLUSA

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