  We prove in this paper that the expected value of the objective function of
the $k$-means++ algorithm for samples converges to population expected value.
As $k$-means++, for samples, provides with constant factor approximation for
$k$-means objectives, such an approximation can be achieved for the population
with increase of the sample size.
  This result is of potential practical relevance when one is considering using
subsampling when clustering large data sets (large data bases).
