ClusCorr98 - Adaptive Clustering, Multivariate Visualization, and Validation of Results

  • Hans-Joachim Mucha
  • Hans-Georg Bartel
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


An overview over a new release of the statistical software ClusCorr98 will be given. The emphasis of this software lies on an extended collection of exploratory and model-based clustering techniques with in-built validation via resampling. Using special weights of observations leads to well-known resampling techniques. By doing so, the appropriate number of clusters can be validated. As an illustration of an interesting feature of ClusCorr98, a general validation of results of hierarchical clustering based on the adjusted Rand index is recommended. It is applied to demographical data from economics. Here the stability of each cluster can be assessed additionally.


Hierarchical Cluster Analysis Special Weight Adjusted Rand Index Weighted Observation Supplementary Object 
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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  1. BANFIELD, J.D. and RAFTERY, A.E. (1993): Model-Based Gaussian and non-Gaussian Clustering. Biometrics, 49, 803–821.MathSciNetGoogle Scholar
  2. CIA World Factbook (1999): Population by Country. Scholar
  3. FRALEY, C. (1996): Algorithms for model-based Gaussian Hierarchical Clustering. Technical Report, 311. Department of Statistics, University of Washington, Seattle.Google Scholar
  4. GORDON, A.D. (1999): Classification. Chapman & Hall/CRC, London.Google Scholar
  5. GOWER, J.C. (1971): A General Coefficient of Similarity and some of its Properties. Biometrics, 27, 857–874.Google Scholar
  6. HUBERT, L.J. and ARABIE, P. (1985): Comparing Partitions. Journal of Classification, 2, 193–218.CrossRefGoogle Scholar
  7. JAIN, A.K. and DUBES, R.C. (1988): Algorithms for Clustering Data. Prentice Hall, New Jersey.Google Scholar
  8. KAUFMAN, L. and ROUSSEEUW, P.J. (1990): Finding Groups in Data. Wiley, New York.Google Scholar
  9. MUCHA, H.-J. (1992): Clusteranalyse mit Mikrocomputern. Akademie Verlag, Berlin.Google Scholar
  10. MUCHA, H.-J., BARTEL, H.-G., and DOLATA, J. (2002a): Exploring Roman Brick and Tile by Cluster Analysis with Validation of Results. In: W. Gaul and G. Ritter (Eds.): Classification, Automation, and New Media. Springer, Heidelberg, 471–478.Google Scholar
  11. MUCHA, H.-J., BARTEL, H.-G., and DOLATA, J. (2003): Core-based Clustering Techniques. In: M. Schader, W. Gaul, and M. Vichi (Eds.): Between Data Science and Applied Data Analysis. Springer, Berlin, 74–82.Google Scholar
  12. MUCHA, H.-J, SIMON, U, and BRÜGGEMANN, R. (2002b): Model-based Cluster Analysis Applied to Flow Cytometry Data of Phytoplankton. Weierstraß Institute for Applied Analysis and Stochastic, Technical Report No. 5. Scholar
  13. RAND, W.M. (1971): Objective Criteria for the Evaluation of Clustering Methods. Journal of the American Statistical Association, 66, 846–850.CrossRefGoogle Scholar
  14. WARD, J.H. (1963): Hierarchical Grouping Methods to Optimise an Objective Function. JASA, 58, 235–244.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Hans-Joachim Mucha
    • 1
  • Hans-Georg Bartel
    • 2
  1. 1.Weierstraß-Institute of Applied Analysis and StochasticBerlinGermany
  2. 2.Institut für ChemieHumboldt-Universität zu BerlinBerlinGermany

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