# 2. Detecting Clusters Graphically

Published Online: 25 JAN 2011

DOI: 10.1002/9780470977811.ch2

Copyright © 2011 John Wiley & Sons, Ltd

Book Title

## Cluster Analysis, 5th Edition

Additional Information

#### How to Cite

Everitt, B. S., Landau, S., Leese, M. and Stahl, D. (2011) Detecting Clusters Graphically, in Cluster Analysis, 5th Edition, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470977811.ch2

#### Publication History

- Published Online: 25 JAN 2011
- Published Print: 7 JAN 2011

#### Book Series:

#### Book Series Editors:

- Walter A. Shewhart and
- Samuel S. Wilks

#### ISBN Information

Print ISBN: 9780470749913

Online ISBN: 9780470977811

- Summary
- Chapter

### Keywords:

- bivariate marginal plots;
- cluster structure;
- graphical techniques;
- multivariate data;
- Scatterplot matrices;
- trellis graphics

### Summary

This chapter describes a number of relatively simple, static graphical techniques that are useful for providing evidence for or against possible cluster structure in the data. Most of the methods are based on an examination of either direct univariate or bivariate marginal plots of the multivariate data, or indirect one- or two-dimensional “views” of the data obtained from the application to the data of a suitable dimension reduction technique. Scatterplots and, to some extent, scatterplot matrices are more useful for exploring multivariate data for the presence of clusters when there are only a relatively small number of variables. The chapter deals with plotting data in some two-dimensional space using either the original variables, or derived variables constructed in some way so that a low-dimensional projection of the data is informative. Trellis graphics and the associated lattice graphics examine high-dimensional structure in data by means of one-, two- and three-dimensional graphs.

#### Controlled Vocabulary Terms

bivariate analysis; cluster analysis; graphical model; multivariate chart; Scatter plot