Preface to First Edition - lib

334 CLUSTER ANALYSIS18.4 SummaryCluster analysis techniques provide a rich source of possible strategies for exploringcomplex multivariate data. But the use of cluster analysis in practisedoes not involve simply the application of one particular technique to the dataunder investigation, but rather necessitates a series of steps, each of which maybe dependent on the results of the preceding one. It is generally impossiblea priori to anticipate what combination of variables, similarity measures andclustering technique is likely to lead to interesting and informative classifications.Consequently, the analysis proceeds through several stages, with theresearcher intervening if necessary to alter variables, choose a different similaritymeasure, concentrate on a particular subset of individuals, and so on. Thefinal, extremely important, stage concerns the evaluation of the clustering solutionsobtained. Are the clusters ‘real’ or merely artefacts of the algorithms?Do other solutions exist that are better in some sense? Can the clusters begiven a convincing interpretation? A long list of such questions might be posed,and readers intending to apply clustering to their data are recommended toread the detailed accounts of cluster evaluation given in Dubes and Jain (1979)and in Everitt et al. (2001).ExercisesEx. 18.1 Construct a three-dimensional drop-line scatterplot of the planetsdata in which the points are labelled with a suitable cluster label.Ex. 18.2 Write an R function to fit a mixture of k normal densities to a dataset using maximum likelihood.Ex. 18.3 Apply complete linkage and average linkage hierarchical clusteringto the planets data. Compare the results with those given in the text.Ex. 18.4 Write a general R function that will display a particular partitionfrom the k-means cluster method on both a scatterplot matrix of the originaldata and a scatterplot or scatterplot matrix of a selected number ofprincipal components of the data.© 2010 by Taylor and Francis Group, LLC