What analytical method assesses how well a data point fits within a cluster relative to others?

Silhouette analysis is an analytical method used to determine how well a data point fits within its assigned cluster compared to other clusters. This technique provides a metric that ranges from -1 to +1, where a higher value indicates that the data point is well-matched to its own cluster and poorly matched to neighboring clusters. This can help to evaluate the appropriateness of the clustering solution, allowing practitioners to understand the separation and cohesion of clusters.

The clustering coefficient, while related to the concept of clusters, measures the degree to which nodes in a graph tend to cluster together rather than assessing the fit of individual data points within clusters. Association rule mining is a technique for discovering interesting relationships between variables in large datasets, which does not involve clustering or assessing point fit. Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms data into a lower-dimensional space, focusing on the variance in the data rather than the clustering of data points. Thus, silhouette analysis specifically addresses the fitting of data points within clusters, making it the most appropriate method among the options listed.