Skip to main content

Initialization Dependence of Clustering Algorithms

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 5507)


It is well known that the clusters produced by a clustering algorithm depend on the chosen initial centers. In this paper we present a measure for the degree to which a given clustering algorithm depends on the choice of initial centers, for a given data set. This measure is calculated for four well-known offline clustering algorithms (k-means Forgy, k-means Hartigan, k-means Lloyd and fuzzy c-means), for five benchmark data sets. The measure is also calculated for ECM, an online algorithm that does not require the number of initial centers as input, but for which the resulting clusters can depend on the order that the input arrives. Our main finding is that this initialization dependence measure can also be used to determine the optimal number of clusters.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-03040-6_75
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-03040-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.00
Price excludes VAT (USA)


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Redmond, S.J., Heneghan, C.: A method for initialising the K-means clustering algorithm using kd-trees. Pattern Recognition Letters 28, 965–973 (2007)

    CrossRef  Google Scholar 

  2. Al-Daoud, M.B., Roberts, S.A.: New methods for the initialisation of clusters. Pattern Recognition Letters 17, 451–455 (1996)

    CrossRef  Google Scholar 

  3. Katsavounidis, I., Kuo, J., Zhen Zhang, C.-C.: A new initialization technique for generalized Lloyd iteration. IEEE Signal Processing Letters 1, 144–146 (1994)

    CrossRef  Google Scholar 

  4. Khan, S.S., Ahmad, A.: Cluster center initialization algorithm for K-means clustering. Pattern Recognition Letters 25, 1293–1302 (2004)

    CrossRef  Google Scholar 

  5. Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31, 264–323 (1999)

    CrossRef  Google Scholar 

  6. Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  7. UC Machine Learning Repository,

  8. SPAETH Cluster Analysis Datasets,

  9. SPAETH2 Cluster Analysis Datasets, .

  10. Davies, D.L., Bouldin, D.W.: A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1, 224–227 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

De Mulder, W., Schliebs, S., Boel, R., Kuiper, M. (2009). Initialization Dependence of Clustering Algorithms. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03039-0

  • Online ISBN: 978-3-642-03040-6

  • eBook Packages: Computer ScienceComputer Science (R0)