Journal of Classification

, Volume 24, Issue 1, pp 99–121

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

  • Douglas Steinley
  • Michael J. Brusco

DOI: 10.1007/s00357-007-0003-0

Cite this article as:
Steinley, D. & Brusco, M. Journal of Classification (2007) 24: 99. doi:10.1007/s00357-007-0003-0


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.

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

Personalised recommendations