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Algorithm::FuzzyCmeans - perl implementation of Fuzzy c-means clustering

=head1 SYNOPSIS

  use Algorithm::FuzzyCmeans;
  
  # input documents
  my %documents = (
      Alex => { 'Pop'     => 10, 'R&B'    => 6, 'Rock'   => 4 },
      Bob  => { 'Jazz'    => 8,  'Reggae' => 9                },
      Dave => { 'Classic' => 4,  'World'  => 4                },
      Ted  => { 'Jazz'    => 9,  'Metal'  => 2, 'Reggae' => 6 },
      Fred => { 'Hip-hop' => 3,  'Rock'   => 3, 'Pop'    => 3 },
      Sam  => { 'Classic' => 8,  'Rock'   => 1                },
  );
  
  my $fcm = Algorithm::FuzzyCmeans->new(
      distance_class => 'Algorithm::FuzzyCmeans::Distance::Cosine',
      m              => 2.0,
  );
  foreach my $id (keys %documents) {
      $fcm->add_document($id, $documents{$id});
  }
  
  my $num_cluster = 3;
  my $num_iter    = 20;
  $fcm->do_clustering($num_cluster, $num_iter);             
  
  # show clustering result
  foreach my $id (sort { $a cmp $b } keys %{ $fcm->memberships }) {
      printf "%s\t%s\n", $id,
          join "\t", map { sprintf "%.4f", $_ } @{ $fcm->memberships->{$id} };
  }
  # show cluster centroids
  foreach my $centroid (@{ $fcm->centroids }) {
      print join "\t", map { sprintf "%s:%.4f", $_, $centroid->{$_} }
          keys %{ $centroid };
      print "\n";
  }

=head1 DESCRIPTION

Algorithm::FuzzyCmeans is a perl implementation of Fuzzy c-means clustering.

=head1 METHODS

=head2 new

Create a new instance.

`m' option is a fuzzyness coefficient, and must be more than 1.0 (default: 2.0).

`distance_class' option is a class name with distance function between vectors. Currently, 'Algorithm::FuzzyCmeans::Distance::Euclid'(euclid distance) and 'Algorithm::FuzzyCmeans::Distance::Cosine'(cosine distance) are supported (default: cosine).

=head2 add_document($id, $vector)

Add an input document to the instance of Algorithm::FuzzyCmeans. $id parameter is the identifier of a document, and $vector parameter is the feature vector of a document. $vector parameter must be a hash reference, each key of $vector parameter is th...

=head2 do_clustering($num_cluster, $num_iter)

Do clustering input documents. $num_cluster parameter specifies the number of output clusters, and $num_iter parameter specifies the number of clustering iterations.

=head2 memberships

This method is the accessor of clustering result. The output of the method is a hash reference, the key is the identifier of each input document, and the value is the list of the degrees of membership of each input document in output clusters.

=head2 centroids

This method is the accessor of the vectors of cluster centroids.

=head1 AUTHOR

Mizuki Fujisawa E<lt>fujisawa@bayon.ccE<gt>

=head1 SEE ALSO

=over

=item Wikipedia: Fuzzy c-means clustering
http://en.wikipedia.org/wiki/Cluster_Analysis#Fuzzy_c-means_clustering

=back

=head1 LICENSE

This library is free software; you can redistribute it and/or modify
it under the same terms as Perl itself.

=cut