Chad A Davis >
Algorithm-DistanceMatrix >
Algorithm::DistanceMatrix

Module Version: 0.04
Algorithm::DistanceMatrix - Compute distance matrix for any distance metric

version 0.04

use Algorithm::DistanceMatrix; my $m = Algorithm::DistanceMatrix->new( metric=>\&mydistance,objects=\@myarray); my $distmatrix = $m->distancematrix; use Algorithm::Cluster qw/treecluster/; # method=> # s: single-linkage clustering # http://en.wikipedia.org/wiki/Single-linkage_clustering # m: maximum- (or complete-) linkage clustering # http://en.wikipedia.org/wiki/Complete_linkage_clustering # a: average-linkage clustering (UPGMA) # http://en.wikipedia.org/wiki/UPGMA my $tree = treecluster(data=>$distmat, method=>'a'); # Get your objects and the cluster IDs they belong to, assuming 5 clusters my $cluster_ids = $tree->cut(5); # Index corresponds to that of the original objects print $objects->[2], ' belongs to cluster ', $cluster_ids->[2], "\n";

This is a small helper package for Algorithm::Cluster. That module provides many facilities for clustering data. It also provides a `distancematrix`

function, but assumes tabular data, which is the standard for gene expression data.

If your data is tabular, you should first have a look at `distancematrix`

in Algorithm::Cluster

http://cpansearch.perl.org/src/MDEHOON/Algorithm-Cluster-1.48/doc/cluster.pdf

Otherwise, this package provides a simple distance matrix, given an arbitrary distance function. It does not assume anything about your data. You simply provide a callback function for measuring the distance between any two objects. It produces a lower diagonal (by default) distance matrix that is fit to be used by the clustering algorithms of Algorithm::Cluster.

Algorithm::DistanceMatrix - Compute distance matrix for any distance metric

version 0.04

One of `qw/lower upper full/`

for a lower diagonal, upper diagonal, or full distance matrix.

Callback for computing the distance, similarity, or whatever measure you like.

$matrix->metric(\@mydistance);

Where `mydistance`

receives two objects as it's first two arguments.

If you need to pass special parameters to your method:

$matrix->metric(sub{my($x,$y)=@_;mydistance(first=>$x,second=>$y,mode=>'fast')};

You may use any metric, and may return any number or object. Note that if you plan to use this with Algorithm::Cluster this needs to be a distance metric. So, if you're measure how similar two things are, on a scale of 1-10, then you should return `10-$similarity`

to get a distance.

Default is the absolute values of the scalar difference (i.e. `abs(X-Y)`

)

Array reference. Doesn't matter what kind of objects are in the array, as long as your `metric`

can process them.

2D array of distances (or similarities, or whatever) between your objects.

(An ArrayRef of ArrayRefs.)

Chad A. Davis <chad.a.davis@gmail.com>

This software is copyright (c) 2011 by Chad A. Davis.

This is free software; you can redistribute it and/or modify it under the same terms as the Perl 5 programming language system itself.

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