Anagha K Kulkarni >
Statistics-Gap-0.10 >
Statistics::Gap

Module Version: 0.10
Statistics::Gap - An adaptation of the "Gap Statistic"

use Statistics::Gap; $predictedk = &gap("prefix", "vec", INPUTMATRIX, "rbr", "h2", 30, 10, rep, 90, 4); OR use Statistics::Gap; $predictedk = &gap("prefix", "vec", INPUTMATRIX, "rbr", "h2", 30, 10, rep, 90, 4, 7);

1. Prefix: The string that should be used to as a prefix while naming the intermediate files and the .dat files (plot files).

2. Space: Specifies the space in which the clustering should be performed. Valid parameter values: vec - vector space sim - similarity space

3. InputMatrix: Path to input matrix file. (More details about the input file-format below.)

4. ClusteringMethod: Specifies the clustering method to be used. (Learn more about this at: http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview)

Valid parameter values: rb - Repeated Bisections rbr - Repeated Bisections for by k-way refinement direct - Direct k-way clustering agglo - Agglomerative clustering bagglo - Partitional biased Agglomerative clustering NOTE: bagglo can be used only if space=vec

5. Crfun: Specifies the criterion function to be used for finding clustering solutions. (Learn more about this at: http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview)

Valid parameter values: i1 - I1 Criterion function i2 - I2 Criterion function e1 - E1 Criterion function h1 - H1 Criterion function h2 - H2 Criterion function

6. K: This is an approximate upper bound for the number of clusters that may be present in the dataset.

7. B: The number of replicates/references to be generated.

8. TypeRef: Specifies whether to generate B replicates from a reference or to generate B references.

Valid parameter values: rep - replicates ref - references

9. Percentage: Specifies the percentage confidence to be reported in the log file. Since Statistics::Gap uses parametric bootstrap method for reference distribution generation, it is critical to understand the interval around the sample mean that could contain the population ("true") mean and with what certainty.

10. Precision: Specifies the precision to be used while generating the reference distribution.

11. Seed: The seed to be used with the random number generator. (This is an optional parameter. By default no seed is set.)

The input matrix can be in either dense or sparse format. The cell values can be integer or real. Depending upon the value specified for the space parameter the header in the input file (first line) changes.

Example of input matrix in dense format when space=vec: The first line specifies the dimensions - #rows #cols From the second line the actual matrix follows.

6 5 1.3 2 0 0 3 2.1 0 4 2.7 0 1.3 2 0 0 3 2.1 0 4 2.7 0 1.3 2 0 0 3 2.1 0 4 2.7 0

Example of input matrix in dense format when space=sim: The matrix, when in similarity space, is square and symmetric. The first line specifies the dimensions - #rows/#cols From the second line the actual matrix follows.

5 1.0000 0.3179 0.5544 0.2541 0.4431 0.3179 1.0000 0.1386 0.4599 0.5413 0.5544 0.1386 1.0000 0.5143 0.2186 0.2541 0.4599 0.5143 1.0000 0.5148 0.4431 0.5413 0.2186 0.5148 1.0000

Example of input matrix in sparse format when space=vec: The first line specifies the dimensions & number of non-zero elements - #rows #cols #nonzeroElem

From the second line the matrix contents follow. Only non-zero elements are specified. Thus the elements are specified as pairs of - #col elem. The row number is implied by the (line number-1).

8 10 41 1 3 4 2 8 2 10 1 1 1 2 5 3 1 5 2 7 1 9 2 1 3 4 2 8 2 10 1 1 1 2 5 3 1 5 2 7 1 9 2 1 3 4 2 8 2 10 1 1 1 2 5 3 1 5 2 7 1 9 2 2 4 3 1 4 2 5 5 7 1 9 1 2 4 3 1 4 2 5 5 7 1 9 1

Example of input matrix in sparse format when space=sim: The first line specifies the dimensions & number of non-zero elements - #rows/#cols #nonzeroElem The matrix format is same as explained above.

5 15 1 1.0000 3 0.5544 5 0.4431 2 1.0000 3 0.1386 4 0.4599 5 0.5413 1 0.5544 2 0.1386 3 1.0000 2 0.4599 4 1.0000 1 0.4431 2 0.5413 5 1.0000

1. A single integer number at STDOUT which is the Gap Statistic's estimate of number of clusters present in the input dataset.

2. The prefix.gap.log file contains the log of various values at different K values. The first table in the file gives values like Gap(k), obs(crfun(k)) etc. for every k value experimented with.

3. The prefix.*.dat files are provided to facilitate generation of plots of the observed distribution, expected distribution and gap(k), if desired.

Given a dataset how does one automatically find the optimal number of clusters that the dataset should be grouped into? - is one of the prevailing problems. Statisticians Robert Tibshirani, Guenther Walther and Trevor Hastie propose a solution for this problem in a Techinal Report named - "Estimating the number of clusters in a dataset via the Gap Statistic". This perl module implements an adaptation of the approach proposed in the above paper.

If one tries to cluster a dataset (i.e. numerous observations described in terms of a feature space) into n groups/clusters and if we plot the graph of within cluster disimilarity (error) or similarity along Y-axis and Number of clusters along X-axis then this graph generally takes a form of a elbow/knee depending upon the measure on the Y-axis. The Gap Statistic seeks to locate this elbow/knee because the value on the X-axis at this elbow is the optimal number of clusters for the data.

To locate this elbow Gap Statistic standardizes the graph of error by comparing it with the expected graph under appropriate null reference distribution. The adopted null model is the case of single cluster (k=1) which is rejected in favor of k (k>1) if sufficient evidence is present. The predicted k is the k value for which the error graph falls the farthest below the reference graph.

As we have seen above, the Gap Statistic uses the within cluster dispersion (error) measure to find the elbow/knee. In this adaptation, we use clustering criterion functions (crfun) instead of within cluster dispersion measure. The crfun are used by the clustering methods to obtain an optimal clustering solution for a given data and number of clusters.

We provide two types of reference generation methods:

1. One can choose to generate a random dataset over the observed distribution by holding the row and the column marginals fixed and then generating B replicates from this random dataset using Monte Carlo sampling.

2. Or to generate B random datasets over the observed distribution by holding the row and the column marginals fixed.

Please refer to http://search.cpan.org/dist/Algorithm-RandomMatrixGeneration/ to learn more about generating random dataset over the observed distribution by holding the row and the column marginals fixed.

"gap" function by default.

1. This module uses suite of C programs called CLUTO for clustering purposes. Thus CLUTO needs to be installed for this module to be functional. CLUTO can be downloaded from http://www-users.cs.umn.edu/~karypis/cluto/

2. Following Perl Modules Math::BigFloat (http://search.cpan.org/dist/Math-BigInt-1.77/) Algorithm::RandomMatrixGeneration (http://search.cpan.org/dist/Algorithm-RandomMatrixGeneration/)

http://citeseer.ist.psu.edu/tibshirani00estimating.html http://www-users.cs.umn.edu/~karypis/cluto/ http://search.cpan.org/dist/Algorithm-RandomMatrixGeneration/

Anagha Kulkarni, University of Minnesota, Duluth kulka020 <at> d.umn.edu Ted Pedersen, University of Minnesota, Duluth tpederse <at> d.umn.edu

Copyright (C) 2006-2008, Anagha Kulkarni and Ted Pedersen

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.

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