- NAME
- SYNOPSIS
- CHANGES
- DESCRIPTION
- METHODS
- HOW ARE THE CLUSTERS OUTPUT?
- REQUIRED
- EXAMPLES
- EXPORT
- CAVEATS
- BUGS
- INSTALLATION
- THANKS
- AUTHOR
- COPYRIGHT

Algorithm::KMeans - Clustering multi-dimensional data with a pure-Perl implementation

use Algorithm::KMeans;

# First name the data file:

my $datafile = "mydatafile.dat";

# Next, set the mask to indicate which columns of the datafile to use for clustering and # which column contains a symbolic ID for each data record. For example, if the symbolic # name is in the first column, if you want the second column to be ignored, and if # you want the next three columns to be used for 3D clustering:

my $mask = "N0111";

# Now construct an instance of the clusterer. The parameter K controls the number # of clusters. If you know how many clusters you want (in this case 3), call

my $clusterer = Algorithm::KMeans->new( datafile => $datafile, mask => $mask, K => 3, terminal_output => 1, );

# If you believe that the individual clusters in your data are not isotropic # (that is, you believe the variances within each cluster are significantly different # along the different dimensions), you may wish for the clusterer to first normalize # the data along each dimension with an estimate for the standard-deviations along # that dimension and then carry out clustering. What estimate to use for such standard # deviations obviously becomes an issue unto itself. In the current implementation, # we use overall data standard-deviation along each dimension as the estimate. # BUT BEWARE THAT IF THE DATA VARIANCE IS CAUSED MORE BY THE SEPARATION BETWEEN THE # MEANS THAN BY THE INTRA-CLUSTER VARIABILITY, THE DATA NORMALIZATION BY THE STANDARD # DEVIATIONS COULD ACTUALLY DECREASE THE PERFORMANCE OF THE CLUSTERER. Here is an # example call to the constructor for turning on the data normalization:

my $clusterer = Algorithm::KMeans->new( datafile => $datafile, mask => $mask, K => 3, terminal_output => 1, do_variance_normalization => 1, );

# Set K to 0 if you want the module to figure out the optimum number of clusters # from the data. (It is best to run this option with the terminal_output set to # 1 so that you can see the different value of QoC for the different K):

my $clusterer = Algorithm::KMeans->new( datafile => $datafile, mask => $mask, K => 0, terminal_output => 1, );

# Althogh not shown above, you can obviously set the 'do_variance_normalization' # flag here also if you wish.

# For very large data files, setting K to 0 will result in searching through # too many values for K. For such cases, you can range limit the values of K to # search through by

my $clusterer = Algorithm::KMeans->new( datafile => $datafile, mask => "N111", Kmin => 3, Kmax => 10, terminal_output => 1, );

# Use the following call if you wish for the clusters to be written out to files. # Each cluster will be deposited in a file named 'ClusterX.dat' with X starting # from 0:

my $clusterer = Algorithm::KMeans->new( datafile => $datafile, mask => $mask, K => $K, write_clusters_to_files => 1, );

# FOR ALL CASES ABOVE, YOU'D NEED TO MAKE THE FOLLOWING CALLS ON THE CLUSTERER # INSTANCE TO ACTUALLY CLUSTER THE DATA:

$clusterer->read_data_from_file(); $clusterer->kmeans();

# If you want to directly access the clusters and the cluster centers in your # top-level script:

my ($clusters, $cluster_centers) = $clusterer->kmeans();

# You can now access the symbolic data names in the clusters directly, as in:

foreach my $cluster (@$clusters) { print "Cluster: @$cluster\n\n" }

# CLUSTER VISUALIZATION:

# You must first set the mask for cluster visualization. This mask tells the # module which 2D or 3D subspace of the original data space you wish to visualize # the clusters in:

my $visualization_mask = "111"; $clusterer->visualize_clusters($visualization_mask);

# SYNTHETIC DATA GENERATION:

# The module has been provided with a class method for generating multivariate # data for experimenting with clustering. The data generation is controlled # by the contents of the parameter file that is supplied as an argument to the # data generator method. The mean and covariance matrix entries in the parameter # file must be according to the syntax shown in the param.txt file in the examples # directory. It is best to edit this file as needed:

my $parameter_file = "param.txt"; my $out_datafile = "mydatafile.dat"; Algorithm::KMeans->cluster_data_generator( input_parameter_file => $parameter_file, output_datafile => $out_datafile, number_data_points_per_cluster => $N );

Version 1.20 includes an option to normalize the data with respect to its variability along the different coordinates before clustering is carried out. This can be a useful option for highly non-isotropic data, that is, the data in which the different coordinate values along the different dimensions vary differently. (BUT BEWARE THAT IF THE OVERALL DATA VARIANCE ALONG A DIMENSION IS CAUSED MORE BY THE SEPARATION BETWEEN THE MEANS THAN BY THE INTRA-CLUSTER VARIABILITY, THE DATA NORMALIZATION OF THE SORT IN VERSION 1.20 COULD ACTUALLY DECREASE THE PERFORMANCE OF THE CLUSTERER.) With version 1.20, you can also visualize the raw data and the normed data to see the effects of data normalization. Another reason for Version 1.20 is to get away from multi-part version numbers like 1.x.x. As I discovered (thanks to an email from Steffen Mueller), it is never a good idea to mix version numbers like 1.1, which look like regular floating-point numbers to Perl, and multi-part version numbers like 1.1.1 (which Perl interprets as 1.001001).

Version 1.1.1 allows for range limiting the values of K to search through. K stands for the number of clusters to form. This version also declares the module dependencies in the Makefile.PL file.

Version 1.1 is a an object-oriented version of the implementation presented in version 1.0. The current version should lend itself more easily to code extension. You could, for example, create your own class by subclassing from the class presented here and, in your subclass, use your own criteria for the similarity distance between the data points and for the QoC (Quality of Clustering) metric, and, possibly a different rule to stop the iterations. Version 1.1 also allows you to directly access the clusters formed and the cluster centers in your calling script.

**Algorithm::KMeans** is a *perl5* module for the clustering
of numerical data in multidimensional spaces. Since the
module is entirely in Perl (in the sense that it is not a
Perl wrapper around a C library that actually does the
clustering), the code in the module can easily be modified
to experiment with several aspects of automatic clustering.
For example, one can change the criterion used to measure
the "distance" between two data points, the stopping
condition for accepting final clusters, the criterion used
for measuring the quality of the clustering achieved, etc.

A K-Means clusterer is a poor man's implementation of the EM
algorithm. EM stands for Expectation Maximization. For the
case of isotropic Gaussian data, the results obtained with a
good K-Means implementation should match those obtained with
the EM algorithm. (When the data is non-isotropic but the
nature of anisotropy is the same for all the clusters, the
results you obtain with a K-Means clusterer may be improved
--- but only under certain circumstances --- by first
normalizing the data appropriately, as can done with the
implementation shown here when you set the
*do_variance_normalization* option in the KMeans
constructor. But, as pointed out elsewhere in this
documentation, such normalization may actually decrease the
performance of the clusterer if the overall data variability
along any dimension is more a result of the separation
between the means than a consequence of intra-cluster
variability.) Clustering with K-Means takes place
iteratively and involves two steps: 1) assignment of data
samples to clusters; and 2) Recalculation of the cluster
centers. The assignment step can be shown to be akin to the
Expectation step of the EM algorithm, and the calculation of
the cluster centers akin to the Maximization step of the EM
algorithm.

Of the two key steps of the K-Means algorithm, the assignment step consists of assigning each data point to that cluster from whose center the data point is the closest. That is, during assignment, you compute the distance between the data point and each of the current cluster centers. You assign the data sample on the basis of the minimum value of the computed distance. The second step consists of re-computing the cluster centers for the newly modified clusters.

Obviously, before the two-step approach can proceed, we need to initialize the both the cluster center values and the clusters that can then be iteratively modified by the two-step algorithm. How this initialization is carried out is very important. The implementation here uses a random number generator to find K random integers between 0 and N where N is the total number of data samples that need to be clustered and K the number of clusters you wish to form. The K random integers are used as indices for the data samples in the overall data array --- the data samples thus selected are treated as seed cluster centers. This obviously requires a prior knowledge of K.

How to specify K is one of the most vexing issues in any approach to clustering. In some case, we can set K on the basis of prior knowledge. But, more often than not, no such prior knowledge is available. When the programmer does not explicitly specify a value for K, the approach taken in the current implementation is to try all possible values between 2 and some largest possible value that makes statistical sense. We then choose that value for K which yields the best value for the QoC (Quality of Clustering) metric. It is generally believed that the largest value for K should not exceed sqrt(N/2) where N is the number of data point to be clustered.

How to set the QoC metric is obviously a critical issue unto itself. In the current implementation, the value of QoC is a ratio of the average radius of the clusters and the average distance between the cluster centers. But note that this is a good criterion only when the data exhibits the same variance in all directions. When the data variance is different directions, but still remains the same for all clusters, a more appropriate QoC can be formulated using other distance metrics such as the Mahalanobis distance.

Every iterative algorithm requires a stopping criterion. The criterion implemented here is that we stop iterations when there is no re-assignment of the data points during the assignment step.

Ordinarily, the output produced by a K-Means clusterer will correspond to a local minimum for the QoC values, as opposed to a global minimum. The current implementation protects against that, but only in a very small way, by trying different randomly selected initial cluster centers and then selecting the one that gives the best overall QoC value.

The module provides the following methods for clustering, for cluster visualization, for data visualization, and for the generation of data for testing a clustering algorithm:

**new()**-
my $clusterer = Algorithm::KMeans->new(datafile => $datafile, mask => $mask, K => $K, terminal_output => 1, write_clusters_to_files => 1, );

A call to

`new()`

constructs a new instance of the Algorithm::KMeans class. When $K is a non-zero positive integer, the module will construct exactly that many clusters. However, when $K is 0, the module will find the best number of clusters to partition the data into.The data file is expected to contain entries in the following format

c20 0 10.7087017086940 9.63528386251712 10.9512155258108 ... c7 0 12.8025925026787 10.6126270065785 10.5228482095349 ... b9 0 7.60118206283120 5.05889245193079 5.82841781759102 ... .... ....

where the first column contains the symbolic ID tag for each data record and the rest of the columns the numerical information. As to which columns are actually used for clustering is decided by the string value of the mask. For example, if we wanted to cluster on the basis of the entries in just the 3rd, the 4th, and the 5th columns above, the mask value would be `N0111' where the character `N' indicates that the ID tag is in the first column, the character '0' that the second column is to be ignored, and the '1's that follow that the 3rd, the 4th, and the 5th columns are to be used for clustering.

The parameter

*terminal_output*is boolean; when not supplied in the call to`new()`

it defaults to 0. When set, this parameter determines what you will see on the terminal screen of the window in which you make these method calls. When set to 1, you will see on the terminal screen the different clusters as lists of the symbolic IDs and their cluster centers. You will also see the QoC (Quality of Clustering) value for the clusters displayed.The parameter

*write_clusters_to_files*is boolean; when not supplied in the call to`new()`

, it defaults to 0. When set to 1, the clusters are written out to files namedCluster0.dat Cluster1.dat Cluster2.dat ... ...

Before the clusters are written to these files, the module destroys all files with such names in the directory in which you call the module.

If you wish for the clusterer to search through a

*(Kmin,Kmax)*range of values for K, the constructor should be called in the following fashion:my $clusterer = Algorithm::KMeans->new(datafile => $datafile, mask => $mask, Kmin => 3, Kmax => 10, terminal_output => 1, );

where obviously you can choose any reasonable values for Kmin and Kmax. If you choose a value for Kmax that is statistically too large, the module will let you know.

If you believe that the individual clusters in your data are very anisotropic (that is, you believe that intra-cluster variability in your data is different along the different dimensions), you might get better clustering by first normalizing the data coordinates by the standard-deviations along those directions. But how to use a reasonable value for such a standard-deviation becomes a big issue unto itself. (The implementation shown here uses the overall data standard-deviation along a direction for the normalization in that direction. As mentioned elsewhere in the documentation, such a normalization could backfire on you if the data variability along a dimension is more a result of the separation between the means than a consequence of the intra-cluster variability.) You can turn on the data normalization by turning on the

*do_variance_normalization*option in the constructor, as inmy $clusterer = Algorithm::KMeans->new( datafile => $datafile, mask => "N111", K => 2, terminal_output => 1, do_variance_normalization => 1, );

**read_data_from_file()**-
$clusterer->read_data_from_file()

**kmeans()**-
$clusterer->kmeans();

or

my ($clusters, $cluster_centers) = $clusterer->kmeans();

The first call above works solely by side-effect. The second call also returns the clusters and the cluster centers.

**get_K_best()**-
$clusterer->get_K_best();

This call makes sense only if you supply either the K=0 option to the constructor, or you specify values for the Kmin and Kmax options. The K=0 and the (Kmin,Kmax) options cause the KMeans algorithm to figure out on its own the best value for K. Remember, K is the number of clusters the data is partitioned into.

**show_QoC_values()**-
$clusterer->show_QoC_values();

presents a table with K values in the left column and the corresponding QoC (Quality-of-Clustering) values in the right column. Note that this call makes sense only if you either supply the K=0 option to the constructor, or you specify values for the

*Kmin*and*Kmax*options. **visualize_clusters()**-
$clusterer->visualize_clusters( $visualization_mask )

The visualization mask here does not have to be identical to the one used for clustering, but must be a subset of that mask. This is convenient for visualizing the clusters in two- or three-dimensional subspaces of the original space.

**visualize_data()**-
$clusterer->visualize_data($visualization_mask, 'original');

$clusterer->visualize_data($visualization_mask, 'normed');

This method requires a second argument and, as shown, it must be either the string 'original' or the string 'normed', the former for the visualization of the raw data and the latter for the visualization of the data after its different dimensions are normalized by the standard-deviations along those directions. If you call the method with the second argument set to 'normed', but do so without turning on the

*do_variance_normalization*option in the KMeans constructor, it will let you know. **cluster_data_generator()**-
Algorithm::KMeans->cluster_data_generator( input_parameter_file => $parameter_file, output_datafile => $out_datafile, number_data_points_per_cluster => 20 );

for generating multivariate data for clustering if you wish to play with synthetic data for clustering. The input parameter file contains the means and the variances for the different Gaussians you wish to use for the synthetic data. See the file param.txt provided in the examples directory. It will be easiest for you to just edit this file for your data generation needs. In addition to the format of the parameter file, the main constraint you need to observe in specifying the parameters is that the dimensionality of the covariance matrix must correspond to the dimensionality of the mean vectors. The multivariate random numbers are generated by calling the Math::Random module. As you would expect, this module requires that the covariance matrices you specify in your parameter file be symmetric and positive definite. Should the covariances in your parameter file not obey this condition, the Math::Random module will let you know.

When the option *terminal_output* is set in the call to the
constructor, the clusters are displayed on the terminal
screen.

When the option *write_clusters_to_files* is set in the
call to the constructor, the module dumps the clusters in
files named

Cluster0.dat Cluster1.dat Cluster2.dat ... ...

in the directory in which you execute the module. The number of such files will equal the number of clusters formed. All such existing files in the directory are destroyed before any fresh ones are created. Each cluster file contains the symbolic ID tags of the data points in that cluster.

This module requires the following two modules:

Math::Random Graphics::GnuplotIF

the former for generating the multivariate random numbers and the latter for the visualization of the clusters.

See the examples directory in the distribution for how to make calls to the clustering and the visualization methods. The examples directory also includes a parameter file, param.txt, for generating synthetic data for clustering. Just edit this file if you would like to generate your own multivariate data for clustering. The parameter file is for the 3D case, but you can generate data with any dimensionality through appropriate entries in the parameter file.

None by design.

Please note that this clustering module is not meant for very large datafiles. Being an all-Perl implementation, the goal here is not the speed of execution. On the contrary, the goal is to make it easy to experiment with the different facets of K-Means clustering. If you need to process a large data file, you'd be better off with a module like Algorithm::Cluster. However note that when you use a wrapper module in which it is a C library that is actually doing the job of clustering for you, it is more difficult to experiment with the various aspects of clustering. At the least, you have to recompile the code for every change you make to the source code of a low-level library. You are spared that frustration with an all-Perl implementation.

Clustering usually does not work well when the data is
highly anisotropic, that is, when the data has very
different variances along its different dimensions. This
problem becomes particularly severe when the different
clusters you expect to see in the data have *non-uniform*
anisotropies. When the anisotropies are uniform, one can
try to improve the performance of a clusterer by first
normalizing the data coordinates along a direction by an
average of the intra-cluster standard-deviations along that
direction. But how to obtain even a rough estimate of such
standard deviations leads you to chicken-and-egg sort of
problems. The current implementation takes the low road
and, when you turn on the data normalization in the KMeans
constructor, normalizes each data coordinate value by the
overall data standard deviation along that direction.
However, as described elsewhere, this may actually reduce
the performance of the clusterer if the data variability
along a direction is more a result of the separation between
the means than because of intra-cluster variability. For
better clustering, one could also try to cluster the data in
a low-dimensional space formed by a principal components
analysis of the data. Depending on how the current module
is received, its future versions may include that
enhancement.

Please notify the author if you encounter any bugs. When sending email, please place the string 'KMeans' in the subject line.

The usual

perl Makefile.PL make make test make install

if you have root access. If not,

perl Makefile.PL prefix=/some/other/directory/ make make test make install

Chad Aeschliman was kind enough to test out the interface of this module and to give suggestions for its improvement. His key slogan: "If you cannot figure out how to use a module in under 10 minutes, it's not going to be used." That should explain the longish Synopsis included here.

Avinash Kak, kak@purdue.edu

If you send email, please place the string "KMeans" in your subject line to get past my spam filter.

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

Copyright 2010 Avinash Kak