Skip to main content

Visualisation of Cluster Analysis Results

  • Conference paper
  • First Online:
Classification and Data Mining

Abstract

We present some methods for (multivariate) visualisation of cluster analysis results and cluster validation results. Visualisation is essential for a better understanding of results because it operates at the interface between statisticians and researchers. Without loss of generality, we focus on visualisation of clustering based on pairwise distances. Here, usually one can start with “dimensionless” heatmaps (fingerprints) of proximity matrices. The Excel “Big Grid” spreadsheet is both a distinguished depository for data/proximities and a plotting board for multivariate graphics such as dendrograms, plot-dendrograms, informative dendrograms and discriminant projection plots. Informative dendrograms are ordered binary trees that show additional information such as stability values of the clusters. In this way, graphics can be a very useful and much simpler aid for the reader.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Bartel, H. -G. (2009). Archäometrische Daten römischer Ziegel aus Germania Superior. In H.-J. Mucha, & G. Ritter (Eds.), Classification and clustering: Models, software and applications (Rep. No. 26), WIAS, Berlin, pp. 50–72.

    Google Scholar 

  • CIA World Factbook. (1999). Population by country. http://www.geographic.org.

  • Hennig, C. (2007). Cluster-wise assessment of cluster stability. Computational Statistics and Data Analysis,52, 258–271.

    Google Scholar 

  • Hubert, L. J., & Arabie, P. (1985). Comparing partitions. Journal of Classification,2, 193–218.

    Google Scholar 

  • Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. Englewood Cliffs. Upper Saddle River, NJ: Prentice-Hall, Inc.

    Google Scholar 

  • Morales-Merino, C., Mucha, H. -J., Bartel, H. -G., & Pernicka, E. (2010). Clay sediments analysis in the troad and its segmentation. In O. Hahn, A. Hauptmann, D. Modarressi-Tehrani, & M. Prange (Eds.), Archäometrie und Denkmalpflege ( Metalla, Sonderheft 3, pp. 122–124). Bochum: Dt. Bergbau-Museum Bochum.

    Google Scholar 

  • Mucha, H. -J. (2004). Automatic validation of hierarchical clustering. In J. Antoch (Ed.), Proceedings in Computational Statistics, COMPSTAT 2004, 16th Symposium (pp. 1535–1542). Heidelberg: Physica-Verlag.

    Google Scholar 

  • Mucha, H. -J. (2007). On validation of hierarchical clustering. In R. Decker, H. -J. Lenz (Eds.), Advances in data analysis (pp. 115–122). Berlin: Springer.

    Google Scholar 

  • Mucha, H. -J. (2009). Cluscorr98 for Excel 2007: Clustering, multivariate visualization, and validation. In H. -J. Mucha, G. Ritter (Eds.), Classification and clustering: Models, software and applications (Rep. No. 26, pp. 14–40). Berlin: WIAS.

    Google Scholar 

  • Mucha, H. -J., Simon, U., & Brüggemann, R. (2002). Model-based cluster analysis applied to flow cytometry data of phytoplankton (Tech. Rep. No. 5). Berlin: WIAS.

    Google Scholar 

  • Mucha, H. -J., Bartel, H. -G., & Dolata, J. (2005). Techniques of rearrangements in binary trees (Dendrograms) and applications. Match,54(3), 561–582.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hans-Joachim Mucha .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mucha, HJ., Bartel, HG., Morales-Merino, C. (2013). Visualisation of Cluster Analysis Results. In: Giusti, A., Ritter, G., Vichi, M. (eds) Classification and Data Mining. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28894-4_31

Download citation

Publish with us

Policies and ethics