CLUTOA Clustering Toolkit

By

Roseline Antai

• CLUTO is a software package which is used for clustering high dimensional datasets and for analyzing the characteristics of the various clusters.

Algorithms of CLUTO

• Vcluster

• Scluster

Major difference: Input

Vcluster: actual mutidimensional representation of the objects to be clustered.

Scluster: The similarity matrix (or graph) between these objects.

Calling Sequence

vcluster [optional parameters] MatrixFileNclusters

scluster [optional parameters] MatrixFileNClusters

Optional Parameters

• Standard specification

-paramname or –paramname = value

• Three categories:

– Clustering algorithm parameters

– Reporting and Analysis parameters

– Cluster Visualization parameters

Clustering algorithm parameters

• Control how CLUTO computes the clustering solution.

• Examples

1. -clmethod=string ( rb, agglo,direct,graph, etc)2. -sim = string (cos,corr,dist,jacc)3. -crfun = string (i1,i2 etc)4. -fulltree

Reporting and Analysis Parameters

• Control the amount of information that vclusterand scluster report about the clusters as well as the analysis performed on discovered clusters.

• Examples

1. -clustfile = string. ( Default is MatrixFile.clustering.Nclusters( or GraphFile))

2. -clabelfile = string (name of the file that’s stores the labels of the columns. Used when –showfeatues, -showsummaries or –labeltree are used)

3. -rlabelfile=string

4. -rclassfile=string (Stores the labels of the rows –objects to be clustered).

5. -showtree

6. -showfeatures (descriptive and discriminating)

Cluster Visualization Parameters

• Simple plots of the original input matrix which show how the different objects (rows) and features (columns) are clustered together.

• Examples

1. -plottree = string; gives graphic representation of the entire hierarchical tree

2. -plotmatrix = string; shows how the rows of the original matrix are clustered together.

A practical example

– ../cluto/Linux/vcluster -clmethod=rb -sim=cos -fulltree -rlabelfile=Final_Results/rlabelfile -rclassfile=Final_Results/classfile -showtree -plotformat=gif -plottree=Final_Results/Images/PT-Final10d -plotmatrix=Final_Results/Images/PM-Final10d -plotclusters=Final_Results/Images/PC-Final10d -showfeaturesFinal_Results/FinalOutput10d-Vt.mat 4

roselineantai@ubuntu:~/JLSI/jlsi$ ./clusterscript2.sh

********************************************************************************

vcluster (CLUTO 2.1.1) Copyright 2001-03, Regents of the University of Minnesota

Matrix Information -----------------------------------------------------------

Name: Final_Results2/FinalOutput50dFinal.mat, #Rows: 59, #Columns: 59, #NonZeros: 3481

Options ----------------------------------------------------------------------

CLMethod=RB, CRfun=I2, SimFun=Cosine, #Clusters: 4

RowModel=None, ColModel=None, GrModel=SY-DIR, NNbrs=40

Colprune=1.00, EdgePrune=-1.00, VtxPrune=-1.00, MinComponent=5

CSType=Best, AggloFrom=0, AggloCRFun=I2, NTrials=10, NIter=10

Solution ---------------------------------------------------------------------

------------------------------------------------------------------------

4-way clustering: [I2=5.65e+01] [59 of 59], Entropy: 0.438, Purity: 0.729

------------------------------------------------------------------------

cid Size ISim ISdev ESim ESdev Entpy Purty | Sem Imp Deo Evo

------------------------------------------------------------------------

0 2 +0.779 +0.000 +0.638 +0.222 0.000 1.000 | 0 2 0 0

1 15 +0.918 +0.024 +0.853 +0.021 0.453 0.800 | 1 0 2 12

2 22 +0.923 +0.015 +0.866 +0.019 0.416 0.818 | 1 18 3 0

3 20 +0.923 +0.015 +0.869 +0.034 0.496 0.550 | 9 0 11 0

------------------------------------------------------------------------

--------------------------------------------------------------------------------

4-way clustering solution - Descriptive & Discriminating Features...

--------------------------------------------------------------------------------

Cluster 0, Size: 2, ISim: 0.779, ESim: 0.638

Descriptive: col00010 32.4%, col00039 22.7%, col00013 5.6%, col00019 3.9%, col00047 2.2%

Discriminating: col00010 53.5%, col00039 22.3%, col00013 2.5%, col00020 2.1%, col00012 1.4%

Cluster 1, Size: 15, ISim: 0.918, ESim: 0.853

Descriptive: col00013 11.3%, col00006 8.2%, col00019 7.9%, col00058 4.4%, col00047 4.2%

Discriminating: col00006 14.8%, col00007 7.4%, col00031 7.1%, col00029 6.6%, col00053 6.2%

Cluster 2, Size: 22, ISim: 0.923, ESim: 0.866

Descriptive: col00013 14.3%, col00020 7.1%, col00014 6.4%, col00019 4.4%, col00047 4.4%

Discriminating: col00020 7.0%, col00014 5.9%, col00013 5.5%, col00006 5.1%, col00010 4.3%

Cluster 3, Size: 20, ISim: 0.923, ESim: 0.869

Descriptive: col00013 8.9%, col00047 6.6%, col00019 5.2%, col00020 4.4%, col00014 3.8%

Discriminating: col00015 8.0%, col00013 7.4%, col00046 5.2%, col00006 5.0%, col00042 4.3%

--------------------------------------------------------------------------------

------------------------------------------------------------------------------

Hierarchical Tree that optimizes the I2 criterion function...

------------------------------------------------------------------------------

Sem Imp Deo Evo

------------------------------------

6

|---4

| |---2 1 18 3 0

| |---3 9 0 11 0

|-5

|-----1 1 0 2 12

|-----0 0 2 0 0

------------------------------------

------------------------------------------------------------------------------

Timing Information -----------------------------------------------------------

I/O: 0.004 sec

Clustering: 0.008 sec

Reporting: 0.316 sec

********************************************************************************

Classfile and rlabelfile

EvoSemImpImpDeoDeoImpImpDeoDeoImpDeoDeoImpSemDeoSemImpImpEvo

0123456789101112131415

The plot uses red to denote positive values and green to denote negative values. Bright red/green indicatelarge positive/negative values, whereas colors close to white indicate values close to zero.

Sem 0Imp 1Deo 2Deo 3Sem 4Evo 5Evo 6Sem 7Imp 8Imp 9Deo 10Deo 11Imp 12Imp 13Deo 14Deo 15Imp 16Deo 17Deo 18Imp 19Sem 20