The distance between clusters (using the chosen linkage method) or variables (using the chosen distance measure) that are joined at each step. Minitab calculates the distance level based on the linkage method and the distance measure that you select in the main dialog box.
The distance between two variables is directly related to their correlation. That is, for two variables, X1 and X2, Distance equals 1− Correlation. For example, if Corr(X1,X2) = 0.879, then Distance(X1,X2) = 1 − 0.879 = 0.121.
Use the distance level for the clusters that are joined at each step to help determine the final groupings for the data. Look for an abrupt change in the distance level between steps. The step that precedes the abrupt change in distance may provide a good cut-off point for the final partition. For the final partition, the clusters should have a reasonably small distance level. You should also use your practical knowledge of the data to determine the final groupings that make the most sense for your application.
For example, the following amalgamation table shows that the distance level increases slightly from step 1 (0.120669) to step 2 (0.136904). The distance then increases more abruptly in step 3 (0.253700), when the number of clusters changes from 3 to 2. These results indicate that 3 clusters may be appropriate for the final partition. If this grouping makes intuitive sense, then it is probably a good choice.
Cluster Analysis of Variables: Newspaper, Radios, TV Sets, Literacy Rat, ...
Correlation Coefficient Distance, Average Linkage
Number of Similarity Distance Clusters New in new
Step clusters level level joined cluster cluster
1 4 93.9666 0.120669 2 3 2 2
2 3 93.1548 0.136904 4 5 4 2
3 2 87.3150 0.253700 1 4 1 3
4 1 79.8113 0.403775 1 2 1 5