A data scientist is evaluating whether to use PROC HPCLUS or PROC CLUSTER for a dataset with 2 million rows. Which statement best justifies the choice of PROC HPCLUS?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you'd pick PROC HPCLUS because it's SAS's high-performance k-means procedure built for large data with multi-threading and distributed processing. PROC CLUSTER does hierarchical clustering, which gets too expensive at 2 million rows.
Full Explanation
PROC HPCLUS is the justified choice because it is SAS's high-performance k-means clustering procedure that leverages multi-threading and can execute in the CAS distributed environment, making it suitable for millions of rows. PROC CLUSTER performs hierarchical clustering, which is O(n^2) and impractical for 2 million rows. The statement that PROC HPCLUS supports hierarchical clustering while PROC CLUSTER does not is wrong because the methods are reversed: PROC CLUSTER does hierarchical clustering. The statement that PROC HPCLUS produces dendrograms while PROC CLUSTER does not is wrong because dendrograms are outputs of hierarchical clustering, which is PROC CLUSTER. The statement that PROC HPCLUS automatically determines the optimal number of clusters while PROC CLUSTER requires k to be specified is wrong because PROC HPCLUS has cluster selection support, not automatic determination as a core requirement.