A SAS analyst uses PROC HPCLUS to cluster 1 million customers. After obtaining cluster assignments, they want to score new customers arriving each week using the cluster centroids. Which approach is most appropriate?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because PROC HPCLUS supports a SCORE statement that applies saved cluster centroids to score new observations by assigning each to its nearest centroid. Alternatively, centroids can be exported to a dataset and a DATA step can compute Euclidean distances and assign clusters.
Full explanation below image
Full Explanation
B is correct because PROC HPCLUS supports a SCORE statement that applies saved cluster centroids to score new observations by assigning each to its nearest centroid. Alternatively, centroids can be exported to a dataset and a DATA step can compute Euclidean distances and assign clusters. A is wasteful and would change cluster definitions each week. C converts an unsupervised problem into a supervised one unnecessarily. D (all pairwise distances) is O(n*m) and impractical for 1 million existing customers.