2008 | OriginalPaper | Chapter
Visualizing Cluster Analysis and Finite Mixture Models
Author : Friedrich Leisch
Published in: Handbook of Data Visualization
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Data visualization can greatly enhance our understanding of multivariate data structures, and so it is no surprise that cluster analysis and data visualization often go hand in hand, and that textbooks like Gordon (1999) or Everitt et al. (2001) are full of figures. In particular, hierarchical cluster analysis is almost always accompanied by a dendrogram. Results frompartitioning cluster analysis can be visualized by projecting the data into two-dimensional space or using parallel coordinates. Cluster membership is usually represented by different colors and glyphs, or by dividing clusters into several panels of a trellis display (Becker et al., 1996). In addition, silhouette plots (Rousseeuw, 1987) provide a popular tool for diagnosing the quality of a partition. Some of the popularity of self-organizing feature maps (Kohonen, 1989) with practitioners in various fields can be explained by the fact that the results can be “easily” visualized.