The Canopy function takes a set of data points and identifies each point with one or more canopies. Canopies are groups of points that are interrelated, close, or similar. Canopy clustering is often performed in preparation for more rigorous clustering techniques, such as k-means clustering.
The canopy clustering
algorithm is nondeterministic, and the randomness of the canopy assignments cannot be
controlled by a seed argument.