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NAME

Algorithm::KernelKMeans - Weighted kernel k-means clusterer

SYNOPSIS

  use Algorithm::KernelKMeans;
  use Algorithm::KernelKMeans::Util qw/generate_polynominal_kernel/;
  use List::MoreUtils qw/zip/;
  use Try::Tiny;
  
  my @vertices = map {
    my @values = split /\s/;
    my @keys = 0 .. $#values;
    +{ zip @keys, @values };
  } (<>);
  my $kernel = generate_polynominal_kernel(1, 2); # K(x1, x2) = (1 + x1x2)^2
  my $wkkm = Algorithm::KernelKMeans->new( # default weights are 1
    vertices => \@vertices,
    kernel => $kernel
  );
  
  my @clusters;
  try {
    @clusters = $wkkm->run(k => 6);
    for my $cluster (@clusters) {
      ...
    }
  } catch {
    # during iteration, number of clusters became less than k_min
    if (/number of clusters/i) { ... }
  }

DESCRIPTION

Algorithm::KernelKMeans provides weighted kernel k-means vector clusterer.

Note that this is a very early release. All APIs may be changed incompatibly.

IMPLEMENTATION

This class is just a placeholder. Implementation code is in other class and this class just inherits it.

Currently there are 2 implementations: Algorithm::KernelKMeans::PP and Algorithm::KernelKMeans::XS.

$Algorithm::KernelKMeans::IMPLEMENTATION indicates which implementation is loaded.

Both of these implements same interface (documented below) and Algorithm::KernelKMeans uses faster (XS) implementation if it's available. So it's not necessary usually to use the classes directly tough, you can do it if you want.

METHODS

new(%opts)

Constructor. you can specify options below:

vertices

Required. Array ref of vectors. Each vector is represented as an hash ref of positive real numbers.

e.g.:

 my $wkkm = Algorithm::KernelKMeans->new(
   vertices => [ [ 229, 151, 42 ], [ 61, 151, 251 ], [ 11, 120, 55 ] ]
 );
]

weights

Array ref of positive real numbers. Defaults to list of 1s.

kernel

Kernel function. The function takes 2 vectors and returns 1 positive real number. Defaults to K(x1, x2) = (1 + x1x2)^2. Algorithm::KernelKMeans::Util has generators for some popular kernel functions.

kernel_matrix

Array ref of array ref of positive real numbers.

A matrix whose element at (i, j) is K(xi, xj) where i >= j. This is derived automatically from kernel by default, however you can specify it manually if you already have it.

Note that the clusterer only uses lower triangle part of the matrix. So it is not necessary for the matrix to have element at (i, j) where i < j (This argument should be called "kernel triangle" rather than "matrix" probably).

Note that kernel and kernel_matrix is exclusive. When you specify kernel_matrix, kernel function is never used.

run(%opts)

Executes clustering. Return value is an array ref of clusters.

k

Required. (maximum) number of clusters.

k_min

Some clusters may be empty during clustering. In the case of that, the clusterer just removes the empty clusters and checks number of rest clusters. If it is less than k_min, the clusterer throws an error.

Default is same as k.

shuffle

When this option is true (this is default), the clusterer sets up initial clusters by random shuffling.

If it is not what you want, you can set false and get always same result:

  use Test::More;
  my $clusters1 = $kkm->run(k => 6, shuffle => 0);
  my $clusters2 = $kkm->run(k => 6, shuffle => 0);
  my $clusters3 = $kkm->run(k => 6);
  is_deeply($clusters1, $clusters2); # ok
  is_deeply($clusters1, $clusters3); # not ok (probably)

converged

Function predicates that clustering is converged. Iteration is broken off and returns result when the predicate returns true.

For each iteration, 2 values are specified: objective function value of current clusters and new clusters' one. As clusters converges, the value decreases.

Default predicate just checks if 2 values are equal.

AUTHOR

Koichi SATOH <sekia@cpan.org>

SEE ALSO

Algorithm::KernelKMeans::PP - Default implementation

Algorithm::KernelKMeans::XS - Yet another implementation. Fast!

LICENSE

The MIT License

Copyright (C) 2010 by Koichi SATOH

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.