In the Hierarchical Clustering report, the Dendrogram section contains a dendrogram and a distance graph. A dendrogram is a tree diagram that represents the agglomeration of observations into clusters. The dendrogram also gives information about the degree of dissimilarity of clusters.
The clustering process can be viewed by reading the dendrogram from left to right. Each step consists of combining the two closest clusters into a single cluster.
• The joining of clusters is indicated by horizontal lines that are connected by vertical lines.
• The horizontal position of the vertical line represents the distance between the two clusters that are most recently joined to form the specified number of clusters.
Note: When the number of observations is less than 256, the distances are proportional to the distances shown in the Distance Graph. Otherwise, Geometric Spacing is used. See Dendrogram Scale.
You can perform the following tasks:
• Click and drag the diamond-shaped handle at either the top or bottom of the dendrogram to identify a given number of clusters.
• Click any cluster stem to select all the members of the cluster in the dendrogram and in the data table.
The Distance Graph is the plot that appears beneath the dendrogram. This graph has a point for each step where two clusters are joined into a single cluster. The horizontal coordinates represent the numbers of clusters, which decrease from left to right. The vertical coordinate of the point is the distance between the clusters that were joined at the given step.
You can click and drag either diamond-shaped handle in the dendrogram to control the chosen number of clusters. When you click and drag the diamond, a vertical line appears in the plot that moves to correspond to the number of clusters. Often there is a point where the slope of the distance graph levels off. Such a point suggests a natural break and helps you determine the number of clusters.
For an example of the relationship between the dendrogram and the distance graph, see Example of Hierarchical Clustering.