This paper describes computational results for k-clustering
algorithm using random sampling technique [2] to show its
practical usefulness. By computational experiments, first,
we show that small size of samples are actually enough for
2-clustering problem. Then, we apply this algorithm for k-
clustering problem in a recursive manner and use the output
as the initial solution of the existing local improvement tech-
nique, called k-means. We compare the result with variance-
based algorithm [1, 4] which is commonly used.