In a SAS k-means clustering workflow using PROC HPCLUS, the pseudo-F statistic (Calinski-Harabasz index) is computed across k=2 through k=10. The pseudo-F values are: k=3: 482, k=4: 716, k=5: 698, k=6: 534. Which k value does the pseudo-F criterion suggest?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because the pseudo-F (Calinski-Harabasz) statistic measures the ratio of between-cluster variance to within-cluster variance. A higher value indicates better-defined clusters.
Full explanation below image
Full Explanation
B is correct because the pseudo-F (Calinski-Harabasz) statistic measures the ratio of between-cluster variance to within-cluster variance. A higher value indicates better-defined clusters. The maximum pseudo-F of 716 occurs at k=4, suggesting that 4 clusters provides the best cluster separation relative to within-cluster compactness. A (k=3) has a lower pseudo-F than k=4. C and D are wrong because the peak is clearly at k=4.