Algorithm-KMeans
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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
## The focus of this script is on the visualization steps after the data has been
## clustered. This script makes calls for the visualization of the data that was
## used for clustering --- both the original data and the data after it is normed
## for variance normalization (assuming you choose the variance normalization step,
## which is not always a good thing).
use strict;
use Algorithm::KMeans;
#my $datafile = "mydatafile1.dat";
my $datafile = "sphericaldata.csv";
my $mask = "N111";
my $clusterer = Algorithm::KMeans->new( datafile => $datafile,
mask => "N111",
cluster_seeding => 'random', # try 'smart' also
K => 3,
terminal_output => 1,
do_variance_normalization => 1,
write_clusters_to_files => 1,
);
$clusterer->read_data_from_file();
my ($clusters_hash, $cluster_centers_hash) = $clusterer->kmeans();
# ACCESSING THE CLUSTERS AND CLUSTER CENTERS IN YOUR SCRIPT:
print "\nDisplaying clusters in the terminal window:\n";
foreach my $cluster_id (sort keys %{$clusters_hash}) {
print "\n$cluster_id => @{$clusters_hash->{$cluster_id}}\n";
}
print "\nDisplaying cluster centers in the terminal window:\n";
foreach my $cluster_id (sort keys %{$cluster_centers_hash}) {
print "\n$cluster_id => @{$cluster_centers_hash->{$cluster_id}}\n";
}
# VISUALIZATION:
# Visualization mask:
# Read the comment block in cluster_and_visualize() that is associated with the
# setting up of the visualization mask.
( run in 0.546 second using v1.01-cache-2.11-cpan-cdf2f3d4e48 )