PROC ACECLUS is run as a preprocessing step before PROC CLUSTER. What does PROC ACECLUS estimate that improves subsequent clustering?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because PROC ACECLUS (Approximate Covariance Estimation for Clustering) estimates a pooled within-cluster covariance structure using a nearest-neighbor approach without knowing the cluster assignments. This covariance estimate is then used in subsequent PROC CLUSTER calls to compute a Mahalanobis-type distance, which accounts for variable correlations and scale differences — improving cluster quality over Euclidean distance on correlated features.
Full explanation below image
Full Explanation
B is correct because PROC ACECLUS (Approximate Covariance Estimation for Clustering) estimates a pooled within-cluster covariance structure using a nearest-neighbor approach without knowing the cluster assignments. This covariance estimate is then used in subsequent PROC CLUSTER calls to compute a Mahalanobis-type distance, which accounts for variable correlations and scale differences — improving cluster quality over Euclidean distance on correlated features. A is wrong; PROC ACECLUS does not compute gap statistics. C is wrong; dimensionality reduction is PROC PRINCOMP's role. D is wrong; PROC ACECLUS does not select linkage criteria.