The graphic display evaluating the quality of a cluster solution comparing two clusters of data is an example of what?

The silhouette statistic is a method used to evaluate the quality of clustering solutions, particularly in identifying how well each object is clustered with respect to other objects. It assesses how similar an object is to its own cluster compared to other clusters, providing a numerical measure ranging from -1 to 1. A silhouette value close to 1 indicates that the data point is well clustered, while a value near 0 suggests that the data point is on or very close to the decision boundary between two neighboring clusters. This graphical display can visually represent the clustering performance by exhibiting the distribution of silhouette values for all clusters, allowing for an easy comparison between the quality of different clustering assignments.

In contrast, the other options represent different types of analytical techniques. The rule induction model pertains to deriving rules that describe the relationships between variables, the classification model is focused on assigning items into predefined categories based on their characteristics, and the association model is designed to find interesting relationships, patterns, or associations among sets of items in large datasets. These approaches do not specifically evaluate the quality of clusters in the manner that the silhouette statistic does.