ex8_kmedoids

这是一个关于c-cluster的技术文章,它详细地介绍了cluster的一些技术分类及算法等,是学习聚类的入门好教程.

#!/usr/perl/perl580/bin/perl -w

use strict;
use Algorithm::Cluster qw/kmedoids distancematrix/;

my $file = "../../data/cyano.txt";
my $i = 0;
my $j = 0;
my (@orfname,@orfdata,@weight,@mask);

open(DATA,"<$file") or die "Can't open file $file: $!";


#------------------
# Read in the data file, and save the data to @orfdata
# We know that the file is intact and has no holes, 
# so just set the mask to 1 for every item.
# We don't check for errors in this case, because the file
# is short and we can spot errors by eye. 
#
my $firstline = <DATA>;  # Skip the title line
while(<DATA>) {

	chomp(my $line = $_);
	my @field     = split /\t/, $line;
	$orfname[$i]  =   $field[0];
	$orfdata[$i]  = [ @field[2..5] ];
	$mask[$i]     = [ 1,1,1,1 ];
	++$i;
}
close(DATA);

#------------------
# Make a reverse-lookup index of the @orfnames hash:
#
my %orfname_by_rowid;
$i=0;
$orfname_by_rowid{$i++} = $_, foreach(@orfname);

@weight = (1.0) x 4;


#------------------
# Define the params we want to pass to distancematrix
my %params1 = (
	transpose =>         0,
	dist      =>       'e',
	data      =>    \@orfdata,
	mask      =>       \@mask,
	weight    =>     \@weight,
);


#------------------
# Here is where we invoke the library function!
#
printf("Calculating the distance matrix\n");

my $matrix = distancematrix(%params1);
#
#------------------

my %params2 = (
        nclusters =>         6,
        distances =>   $matrix,
	npass     =>      1000,
);

printf("Executing k-medoids clustering 1000 times, using random initial clusterings\n");

my ($clusters, $error, $found) = kmedoids(%params2);

my $item;
$i = 0;
foreach $item (@{$clusters}) {
	print $i, ": ", $item, "\n";
	++$i;
}

#------------------
# Print out the resulting within-cluster sum of distances.
#
print "------------------\n";
printf("Within-cluster sum of distances:  %f; solution was found %d times\n\n", $error, $found);
                                                                                
                                                                                
#------------------
# Try this again with a specified initial clustering solution
#

my @initialid = (0,1,2,3,4,5) x 15;
# choice for the initial clustering; the data file contains 90 genes.
                                                                                
%params2 = (
        nclusters =>         6,
        distances =>   $matrix,
        initialid =>  \@initialid,
);
                                                                                
printf("Executing k-medoids clustering with a specified initial clustering\n");
                                                                                
($clusters, $error, $found) = kmedoids(%params2);
                                                                                
printf("Within-cluster sum of distances:  %f\n\n", $error);