I have seen other users ask about recreating SAS's CCC output in other programs. This question, Cubic clustering criterion in R, has an answer that says to use
NbClust to calculate, but that function does not handle large datasets well. It makes a call to
dist that must allocate a 50 gig object. I have tried replacing the function with
proxy::dist from this SO question with the same memory problems.
dist call altogether may be the best option. I am looking to other options to recreate it. In this question How to define number of clusters in K-means clustering?, a user goes through the math provided by SAS. But I do not have the stats chops to translate that into R code.
Keeping it simple, I have
kmeans output that provides total sum of squares (tot.ss), within.ss, between.ss, and I also calculated the $R^2$.
kmeans(x = mydata, centers = 23, iter.max = ITER) Within cluster sum of squares by cluster:  91248.77 72122.06 78680.32 90402.25 86341.35 153533.51 73988.63 64903.32  38334.98 84125.14 92366.93 74721.24 110313.76 96859.55 84516.37 56068.08  76201.69 86194.35 59526.00 53709.75 72503.21 50767.36 80531.94 (between_SS / total_SS = 36.5 %) Available components:  "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss"  "size" "iter" "ifault"
Can I calculate the CCC using these measures?
The second question has a long description from the SAS pdf. But I saw a simplified equation here.
where $E(R^2)$ is the expected $R^2$, and $R^2$ is the observed $R^2$, and $K$ is the variance-stabilizing transformation.
*Can this equation be completed by R's
kmeans output and a calculated $R^2$
One reason why I am focusing on
kmeans is that SAS users utilize
PROC FASTCLUS when running large datasets. It is equivalent to R's kmeans function. The package
NbClust calculates the CCC that I'm looking for, but it does it on the full data with euclidean distance, which is impossible for most computers. That is equivalent to SAS's