kmeans: Quick-TRANSfer stage steps exceeded maximum

Question

I am running k-means clustering in R on a dataset with 636,688 rows and 7 columns using the standard stats package: kmeans(dataset, centers = 100, nstart = 25

Answer 1

Had the same problem, seems to have something to do with available memory.

Running Garbage Collection before the function worked for me:

gc()

or reference:

Increasing (or decreasing) the memory available to R processes

Answer 2

I got the same error message, but in my case it helped to increase the number of iterations iter.max. That contradicts the theory of memory overload.

Answer 3

@jlhoward's comment:

Try

kmeans(dataset, algorithm="Lloyd", ..)

Answer 4

I just had the same issue.

See the documentation of kmeans in R via ?kmeans:

The Hartigan-Wong algorithm generally does a better job than either of those, but trying several random starts (‘nstart’> 1) is often recommended. In rare cases, when some of the points (rows of ‘x’) are extremely close, the algorithm may not converge in the “Quick-Transfer” stage, signalling a warning (and returning ‘ifault = 4’). Slight rounding of the data may be advisable in that case.

In these cases, you may need to switch to the Lloyd or MacQueen algorithms.

The nasty thing about R here is that it continues with a warning that may go unnoticed. For my benchmark purposes, I consider this to be a failed run, and thus I use:

if (kms$ifault==4) { stop("Failed in Quick-Transfer"); }

Depending on your use case, you may want to do something like

if (kms$ifault==4) { kms = kmeans(X, kms$centers, algorithm="MacQueen"); }

instead, to continue with a different algorithm.

If you are benchmarking K-means, note that R uses iter.max=10 per default. It may take much more than 10 iterations to converge.