K-Means clustering generates a specific number of disjoint, flat
(non-hierarchical) clusters. It is well suited to generating globular
clusters. The K-Means method is numerical, unsupervised, non-deterministic
and iterative.

 * There are always K clusters.
 * There is always at least one item in each cluster.
 * The clusters are non-hierarchical and they do not overlap.
 * Every member of a cluster is closer to its cluster than any other cluster
   because closeness does not always involve the 'center' of clusters.

The dataset is partitioned into K clusters and the data points are randomly
assigned to the clusters resulting in clusters that have roughly the same
number of data points.  For each data point: Calculate the distance from the
data point to each cluster.

If the data point is closest to its own cluster, leave it where it is. If the
data point is not closest to its own cluster, move it into the closest
cluster.

Repeat the above step until a complete pass through all the data points
results in no data point moving from one cluster to another. At this point
the clusters are stable and the clustering process ends.  The choice of
initial partition can greatly affect the final clusters that result, in
terms of inter-cluster and intracluster distances and cohesion.