Clustering

[#1]

Use Hierarchical or K-Means Clustering to form clusters (groups) of observations having similar characteristics.

Hierarchical Clustering

1. From an open JMP^® data table, select Analyze > Clustering > Hierarchical Cluster.
2. Select one or more numeric variables from Select Columns and click Y, Columns. Here we used the 13 numeric variables.
3. If available, select a Label variable.
4. Select the desired method (bottom left corner) and click OK. JMP will generate:

• A dendrogram, showing the clusters formed at each step.
• A scree plot, showing the distance bridged each step.
• The clustering history, giving cluster statistics for each step.

Cereal.jmp (Help > Sample Data Folder)

Tips:

• To color clusters, to mark or save clusters, or to request other options, click the top red triangle.
• To dynamically change the number of clusters, click and drag one of the black diamonds left or right.

K-Means Clustering

1. From an open JMP data table, select Analyze > Clustering > K Means Cluster.
2. Select one or more numeric variables from Select Columns and click Y, Columns. Here we used 13 numeric variables. Click OK.
3. In the resulting Control Panel, choose K Means Cluster Under Method.
4. Enter the number of clusters. Click Go. Here we chose 3. JMP will generate:

• A summary of the cluster sizes.
• Tables of cluster means and standard deviations for each variable.

Tips:

• To obtain biplots, parallel plots or request other options, click the red triangle for the K Means heading.
• To perform analyses for a range of cluster sizes: In the Control Panel, enter the lower limit in number of clusters and the upper limit in range of clusters, then click Go.
• To step through the formation of the clusters: In the Control Panel, check Single Step then click Go.

Visit Multivariate Methods > Hierarchical Cluster and K Means Cluster in JMP Help to learn more.