Examples directory:
1)  For the most basic clustering (with fixed K):
        cluster_and_visualize.pl
    The more time you spend with this script, the more comfortable you will
    become with the use of this module. The script file contains a large
    comment block that talks about the SIX LOCATIONS in the script where
    you have to make decisions about how to use the module.
2)  For clustering with an unknown value for K:
       find_best_K_and_cluster.pl
    This script is about the `K=0' option in the constructor that causes
    the module to search for the best K for your data.  Since this search
    is virtually unbounded --- subject only the size of your data file ---
    it may a long time to run this script on a large data file.  Hence the
    next script.
3)  If your datafile is too large, you may need to range limit the values of
    K that are searched through
       find_best_K_in_range_and_cluster.pl
4)  If you also want to include data normalization (it may reduce the
    performance of the clusterer in some cases):
       cluster_after_data_normalization.pl
5)  When you include the data normalization step and you would like to
    visualize the data before and after normalization:
       cluster_and_visualize_with_data_visualization.pl*
6)  After you are done clustering, let's say you want to find the cluster 
    membership of a new data element. To see how you can do that, see the
    script
       which_cluster_for_new_data.pl
    As written, the script gives you two answers for which cluster the new
    data element belongs to.  One of these is using the Euclidean metric to
    calculate the distances between the new data element and the cluster
    centers, and the other using the Mahalanobis metric.  If the clusters
    are strongly elliptical in shape, you are likely to get better results
    with the Mahalanobis metric.
    To see that you can get two different answers using the two different
    distance metrics mentioned above, run the which_cluster_for_new_data.pl
    script on the data in the file mydatafile3.dat.  To make this run, note
    that you have to comment out and uncomment the lines at FOUR different
    locations in the script.
=================================================================================
Support scripts:
7)  For generating the data for experiments with clustering
       data_generator.pl
8)  For cleaning up the examples directory
       cleanup_directory.pl