Cluster Interpretation: Posterior Mean Analysis

Background & Context

On this page, we present the Posterior Mean Analysis for cluster interpretation as an alternative to Most Relevant Explanations for Cluster Interpretation.
To provide further context for Most Relevant Explanations for Cluster Interpretation, we compare several other approaches that can help interpret individual Clusters:
- Setting Evidence for Cluster Interpretation: Posterior Distributions, Relationship with Target Node, Mosaic Analysis, Posterior Mean Analysis, Segment Profile Analysis, Histograms, Tornado Diagrams,
- Optimization for Cluster Interpretation: Dynamic Profile, Target Optimization Tree
More specifically, we compare all these approaches with regard to characterizing the state Cluster 3 of the Cluster Node $Factor\_0\$ in the reference network.
All analyses and instructions on this page refer to this reference network, which you can download here:

MaleClusters.xbl

Given that all manifest nodes feature numerical values, the Posterior Mean Analysis lends itself to interpreting the Clusters.
To start the Posterior Mean Analysis, select Main Menu > Analysis > Report > Target > Posterior Mean Analysis.
The above screenshot shows the report section in which the Posterior Delta Mean Values are displayed. This means that values are displayed as a difference to the population mean for each row.
Furthermore, the values are color-coded: Red marks the lowest value in each row, and green marks the highest. Values close to the row mean value are highlighted in yellow.
This enables a quick-and-easy visual interpretation:
- Individuals in Cluster C1 are on the bottom end of the range for most measurements, while members of C5 seem to have consistently high values.
- Looking at Cluster C3, we observe a contrast: high values for upper body measurements, but only below-average values for lower body measures.

Clicking the Radar Chart button at the bottom of the Posterior Analysis Report window opens up a new window.
The Radar Chart provides another way of visually interpreting the Cluster differences.
To compare specific Clusters, we can focus on a subset, such as C3 and the Prior Values (Mean Values).
To focus on differences, we can furthermore change the sort order of the variables. Here, we order the nodes according to the difference between the Prior and Cluster C3.
This view highlights the strength of C3 members in terms of the upper body and the below-average values for the lower torso and the lower extremities.