  In $K$-means classification, a set of data will form clusters, i.e. classes,
if the measured distances between data points (or some common point in each
class) are below a certain threshold. With the assumption that the data points
are randomly generated throughout some bounded region according to a certain
probability distribution, we estimate the mean number of classes to form with
high probability.
