Step 1: Examine the similarity and distance levels
At each step in the amalgamation process, view the clusters that are formed and examine their similarity and distance levels. The higher the similarity level, the more similar the observations are in each cluster. The lower the distance level, the closer the observations are in each cluster.
Ideally, the clusters should have a relatively high similarity level and a relatively low distance level. However, you must balance that goal with having a reasonable and practical number of clusters.
Step 2: Determine the final groupings for your data
Use the similarity 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 similarity level between steps. The step that precedes the abrupt change in similarity may provide a good cut-off point for the final partition. For the final partition, the clusters should have a reasonably high similarity 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 similarity level decreases by increments of approximately 3 or less until step 15. The similarity decreases by more than 20 (from 62.0036 to 41.0474) at steps 16 and 17, when the number of clusters changes from 4 to 3. These results indicate that 4 clusters may be sufficient for the final partition. If this grouping makes intuitive sense, then it is probably a good choice.
Step 3: Examine the final partition
After you determine the final groupings in step 2, rerun the analysis and specify the number of clusters (or the similarity level) for the final partition. Minitab displays the final partition table, which shows the characteristics of each cluster in the final partition. For example, the average distance from the centroid provides a measure of the variability of the observations within each cluster.
Examine the clusters in the final partition to determine whether the grouping seems logical for your application. If you are still unsure, you can repeat the analysis, and compare dendrograms for different final groupings, to decide which final grouping is the most logical for your data.