Preface to First Edition - lib

318 CLUSTER ANALYSISTable 18.2:planets data (continued).mass period eccen mass period eccen1.150 2614.000000 0.0000 7.500 2300.000000 0.39501.230 1326.000000 0.1400 7.700 58.116000 0.52901.240 391.000000 0.4000 7.950 1620.000000 0.22001.240 435.600000 0.4500 8.000 1558.000000 0.31401.282 7.126200 0.1340 8.640 550.650000 0.71001.420 426.000000 0.0200 9.700 653.220000 0.41001.550 51.610000 0.6490 10.000 3030.000000 0.56001.560 1444.500000 0.2000 10.370 2115.200000 0.62001.580 260.000000 0.2400 10.960 84.030000 0.33001.630 444.600000 0.4100 11.300 2189.000000 0.34001.640 406.000000 0.5300 11.980 1209.000000 0.37001.650 401.100000 0.3600 14.400 8.428198 0.27701.680 796.700000 0.6800 16.900 1739.500000 0.22801.760 903.000000 0.2000 17.500 256.030000 0.42901.830 454.000000 0.2000Source: From Mayor, M., Frei, P.-Y., and Roukema, B., New Worlds in theCosmos, Cambridge University Press, Cambridge, England, 2003. With permission.18.2 Cluster AnalysisCluster analysis is a generic term for a wide range of numerical methods forexamining multivariate data with a view to uncovering or discovering groupsor clusters of observations that are homogeneous and separated from othergroups. In medicine, for example, discovering that a sample of patients withmeasurements on a variety of characteristics and symptoms actually consistsof a small number of groups within which these characteristics are relativelysimilar, and between which they are different, might have important implicationsboth in terms of future treatment and for investigating the aetiologyof a condition. More recently cluster analysis techniques have been appliedto microarray data (Alon et al., 1999, among many others), image analysis(Everitt and Bullmore, 1999) or in marketing science (Dolnicar and Leisch,2003).Clustering techniques essentially try to formalise what human observers doso well in two or three dimensions. Consider, for example, the scatterplotshown in Figure 18.1. The conclusion that there are three natural groups orclusters of dots is reached with no conscious effort or thought. Clusters areidentified by the assessment of the relative distances between points and inthis example, the relative homogeneity of each cluster and the degree of theirseparation makes the task relatively simple.Detailed accounts of clustering techniques are available in Everitt et al.(2001) and Gordon (1999). Here we concentrate on three types of cluster-© 2010 by Taylor and Francis Group, LLC