Clustering is a multivariate technique that groups observations together that share similar values across a number of variables. Hierarchical clustering combines rows in a hierarchical sequence that is portrayed as a tree. Cereals with certain characteristics, such as high fiber, are grouped in clusters so that you can view similarities among cereals.
1.
With Cereal.jmp displayed, select Analyze > Clustering > Hierarchical Cluster.
2.
Select Calories through Enriched, click Y, Columns, and then click OK.
The Hierarchical Clustering report appears. Figure 4.119 shows a portion of the report. The clusters are colored according to the data table row states.
Figure 4.119 Portion of the Hierarchical Clustering Report
3.
Select Color Clusters from the Hierarchical Clustering red triangle menu.
Figure 4.120 Colored Clusters
Figure 4.121 Similar Cereals in Cluster One
Figure 4.122 Selecting a Cluster
5.
To see the similar characteristics in the cluster, select Cluster Summary from the red triangle menu.
Figure 4.123 Cluster Summary
Figure 4.124 Cluster One Characteristics

Help created on 7/12/2018