The Cluster Viewer

Cluster models are typically used to find groups (or clusters) of similar records based on the variables examined, where the similarity between members of the same group is high and the similarity between members of different groups is low. The results can be used to identify associations that would otherwise not be apparent. For example, through cluster analysis of customer preferences, income level, and buying habits, it may be possible to identify the types of customers who are more likely to respond to a particular marketing campaign.

There are two approaches to interpreting the results in a cluster display:

  • Examine clusters to determine characteristics unique to that cluster. Does one cluster contain all the high-income borrowers? Does this cluster contain more records than the others?
  • Examine fields across clusters to determine how values are distributed among clusters. Does one's level of education determine membership in a cluster? Does a high credit score distinguish between membership in one cluster or another?

Using the main views and the various linked views in the Cluster Viewer, you can gain insight to help you answer these questions.

Who uses clustering?

Clustering techniques are useful in a wide variety of situations, including:

  • Market Segmentation. Identify distinct groups among a customer base, allowing precise targeting of sales efforts.
  • Product Bundling. Identify groups of products that tend to appeal to specific customer types.
  • Formal Classification. Classify groups, such as plants or animals into formal taxonomies.
  • Medical Diagnosis. Use biological patterns to uncover rules for identifying or diagnosing medical disorders.

To see information about the cluster model, activate (double-click) the Model Viewer object in the Viewer.