In doing clustering we may be interesting to see if one data point can belong to more than one cluster
but with different degree or probability. Fuzzy clustering can help to do this. Fuzzy k-means algorithm implemented in perl gives the cluster centers and the matrix of probabilities of belonging
to each cluster for each data point. Theory of algorithm can be found in [2].
Here is the numeric example that was used for testing fuzzy clustering script.
Thus from the data output we can see that data point 6 (3,5,4) more close to cluster 1 than data point 1 (3,3,3)
because 0.0377 > 0.009.
