Graphics Reference
In-Depth Information
ter five the correlation is much weaker. SPD and GRUENE are weakly correlated in
both clusters;thedifferencehereismainly the variance: GRUENEhasamuchhigher
variance in cluster four than in cluster five.
Whileall ofthesepatterns canofcoursealsobeinferredfromnumerical printouts
of the component parameters, they are made much more obvious by using graphical
techniques.Completescatterplotmatricesofallpairwisecombinationscanbeusedto
findinteresting variable combinations. Figures
.
and
.
could beaugmented by
aneighborhoodgraph similar tothose used inSect.
.
.Anatural choicetomeasure
the proximity of the mixture components is the Kullback-Leibler-divergence of the
component distributions, see Leisch (
) for details.
Summary
11.5
he natural visualization method for hierarchical clustering is the cluster dendro-
gram, because it directly reflects the construction principle of the underlying algo-
rithm.Similarly,thevisualization ofSOMSistightly bundledwiththealgorithm, and
feature maps on three-dimensional grids are a de facto standard.
Two larger groups of plots are available for partitioning and model-based clus-
tering: the first group are diagnostic plots like silhouettes and posterior rootograms,
which try to visualize the quality of the clustering. he second group of plots sim-
ply treats cluster membership as a categorical variable, and uses standard techniques
likeglyphs, colorsortrellisdisplaystohighlightclustermembershipinvisualizations
of the orginal data (or projections thereof). Virtually all of the methods of plotting
a group of variables against a single categorical variable proposed in this handbook
canbeusedforthispurpose.heexamples showninthischapterarepopularchoices,
but are primarily intended as a starting point and source of inspiration.
References
Becker, R., Cleveland, W. and Shyu, M.-J. (
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Everitt, B.S.,Landau, S.and Leese, M.(
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Fraley,C.andRatery,A.E.(
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Friendly,M.(
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Gordon,A.D.(
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Hartigan, J.A. (
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Hartigan, J.A. and Kleiner, B. (
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Statistician
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Hartigan, J.A. and Wong,M.A. (
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:A k-means clustering algo-
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