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The results of the cluster analysis shown below have been obtained from a set of 20 observations (objects) with four variables by applying Ward's algorithm (ClusterMethod = cmWard) to it.
distance measure: Dice coefficient
clustering method: Ward's method
Obj.1 Obj.2 New Cluster Distance
2 19 21 5.0945
1 16 22 5.3573
3 6 23 7.2815
9 10 24 10.2774
8 14 25 10.6847
12 18 26 13.0239
4 25 27 13.5628
24 15 28 16.0441
5 13 29 16.5704
7 17 30 19.2583
23 27 31 24.1079
11 29 32 24.2236
26 20 33 24.6635
22 21 34 26.9456
31 34 35 39.2175
32 28 36 52.7880
36 30 37 90.4147
35 33 38 109.4378
37 38 39 315.1660
The table above is to interpret as follows: clusters (objects) 2 and 19 are joined to form the new cluster 21; the distance between the two original clusters is 5.09. Next, clusters 1 and 16 are joined to form cluster 22 at a distance of 7.28, and so on. Note that any cluster numbers below or equal to Data.NrOfRows designate the original objects, whereas higher numbers designate clusters built up of other objects and/or clusters.
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