  In this paper we presented a stochastic version mean-shift clustering
algorithm. In the stochastic version the data points "climb" to the modes of
the distribution collectively, while in the deterministic mean-shift, each
datum "climbs" individually, while all other data points remains in their
original coordinates. Stochastic version of the mean-shift clustering is
comparison with a standard (deterministic) mean-shift clustering on synthesized
2- and 3-dimensional data distributed between several Gaussian component. The
comparison performed in terms of cluster purity and class data purity. It was
found the the stochastic mean-shift clustering outperformed in most of the
cases the deterministic mean-shift.
