Algorithm-KMeans

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examples/cluster_after_data_normalization.pl  view on Meta::CPAN

#!/usr/bin/perl -w

#use lib '../blib/lib', '../blib/arch';

##  cluster_after_data_normalization.pl

##  IMPORTANT:  Read the 6 point customization of a script like this in the file:
##
##                            cluster_and_visualize.pl


##  This script demonstrates the use of the 
##

examples/cluster_and_visualize.pl  view on Meta::CPAN

#!/usr/bin/perl -w

#use lib '../blib/lib', '../blib/arch';


##  cluster_and_visualize.pl

##  This is the most basic script in the `examples' directory of the Algorithm::KMeans
##  module.  This script shows how the module is supposed to be called for clustering 
##  your data file.  You must experiment with all of the different options at the
##  six locations mentioned below in order to become more familiar with the capabilities
##  of the module.

examples/cluster_and_visualize_with_data_visualization.pl  view on Meta::CPAN

#!/usr/bin/perl -w

#use lib '../blib/lib', '../blib/arch';


##  cluster_and_visualize_with_data_visualization.pl



##  IMPORTANT:  Read the 6 point customization of a script like this in the
##              file
##                       cluster_and_visualize.pl

examples/data_generator.pl  view on Meta::CPAN

#!/usr/bin/perl -w

#use lib '../blib/lib', '../blib/arch';

use strict;
use Algorithm::KMeans;

# The Parameter File:

# How the synthetic data is generated for clustering is
# controlled entirely by the input_parameter_file keyword in
# the function call shown below.  The mean vector and
# covariance matrix entries in file must be according to the

examples/find_best_K_and_cluster.pl  view on Meta::CPAN

#!/usr/bin/perl -w

#use lib '../blib/lib', '../blib/arch';

##  find_best_K_and_cluster.pl


##  IMPORTANT:  Read the 6 point customization of a script like this in the file:
##
##                       cluster_and_visualize.pl

examples/find_best_K_in_range_and_cluster.pl  view on Meta::CPAN

#!/usr/bin/perl -w

#use lib '../blib/lib', '../blib/arch';


##  find_best_K_in_range_and_cluster.pl


##  IMPORTANT:  Read the 6 point customization of a script like this in the
##              file:
##                            cluster_and_visualize.pl

examples/which_cluster_for_new_data.pl  view on Meta::CPAN

#!/usr/bin/perl -w

#use lib '../blib/lib', '../blib/arch';


##  which_cluster_for_new_data.pl

##  Let's say that after you are done with the clustering of your data, you have a
##  new data element and you want to find out as to which cluster it belongs to.
##  This script demonstrates how you can do that by making calls to the following
##  two methods of the module:
##
##        which_cluster_for_new_data_element()

t/test.t  view on Meta::CPAN

use Test::Simple tests => 3;

use lib '../blib/lib','../blib/arch';

use Algorithm::KMeans;

# Test 1 (Data Generation):

my $datafile = "__testdata.dat";
Algorithm::KMeans->cluster_data_generator( 
                        output_datafile => $datafile,
                        number_data_points_per_cluster => 20 );
open IN, $datafile;