Title
Visualizing cluster analysis and finite mixture models
Document Type
Book Chapter
Abstract
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.
RIS ID
26384
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Publication Details
Leisch, F. (2008). Visualizing cluster analysis and finite mixture models. In C. Chen, W. Hardle & A. Unwin (Eds.), Handbook of data visualization (Springer handbooks of computational statistics) (pp. 561-587). Berlin: Springer.