findModelCluster {svcR}R Documentation

Computation of clustering model by support vector machine

Description

SvcR implements a clustering algorithm based on separator search in a feature space between points described in a data space. Data format is defined by an attribute/value table (matrix). The data are transformed within a kernel to a feature space into a unic cluster bounded with a ball radius and support vectors. We can used the radius of this ball in the data space to reconstruct the boundary shaped now in several clusters.

Usage

## S4 method for signature 'integer':
findModelCluster(x=as.integer(1), MetLab=1, KernChoice=1, Nu=0.8, q=20, K=1, G=10, Cx=1, Cy=2, DName="iris", fileIn="")

## S4 method for signature 'character':
findModelCluster.chargeMatrix( x="term", fileIn="c:/files")

## S4 method for signature 'matrix':
findModelCluster.Eval( x=matrix() )

## S4 method for signature 'numeric':
findModelCluster.Test()

Arguments

x means MetOpt parameter in standard use means DName in chargeMatrix use means DatMat in Eval use, a Matrix given as unic argument
MetLab option taking value 1 (grid labelling) or 2 (mst labelling) or 3 (knn labelling)
KernChoice option taking value 0 (Euclidian) or 1 (RBF) or 2 (Exponential)
Nu kernel parameter
q kernel parameter
K number of neigbours on the grid
G size of the grid
Cx 1st data coordinate to plot for 2D cluster extraction
Cy 2nd data coordinate to plot for 2D cluster extraction
DName Name of data which is the prefix of files : ‘DName_mat.txt’, ‘DName_att.txt’, ‘DName_var.txt
fileIn path where to find files as "D:R\library\svcR\"

Details

format of ‘DName_mat.txt’ (data matrix): 1 1 5.1 1 2 3.5 2 3 1.4 it mean mat[1, 1] = 5.1, mat[1, 2] = 3.5, mat[2, 3] = 1.4

format of ‘DName_att.txt’ : X1 X2 it mean X1 is the name of first column of the data matrix, X2 is the name of the second column of the data matrix

format of ‘DName_var.txt’ : v1 v2 it mean v1 is the name of first line of the data matrix, v2 is the name of the second line of the data matrix

Value

An S4 object of class findModelCluster The object is the svc model along with the slots :

WVectorsYA lagrange coefficients : WVectorsYA$A
Matrice variables names Matrice$var, attributes names Matrice$Att and data Matrice$Mat
MatriceK kernel matrix
Data Data Matrix
MinMaxXY min max values for first and second coordinates
MisClass missclassfied points
DName prefix name of data for files decoding
fileIn data file path
ClassPoints class of grid points
Cx x column id of data matrix
Cy y column id of data matrix
Nu nu value of the svc model
KNN knn value for labelling
SizeGrid size grid for labelling
AroundNullVA almost null value for lagrange coefficient estimation
NumPoints value fo grid points


slots can be accessed by object@slot

Author(s)

Nicolas Turenne - INRA France nicolas.turenne@jouy.inra.fr

References

N.Turenne , Some Heuristics to speed-up Support Vector Clustering , technical report 2006, INRA, France http://migale.jouy.inra.fr/~turenne/svc.pdf

Examples


## exemple with iris data

MetOpt  = 1;    # optimisation method with randomization
MetLab  = 1;    # grid labelling
KChoice = 1;    # 0: eucli 1: radial 2: radial+dist 
Nu      = 1.0; 
q       = 2000;   # lot of clusters
K       = 1;    # only 1  nearest neighbour for clustering
Cx = Cy = 0; # we use principal component analysis factors
G       = 13; # size of the grid for cluster labelling
DName   = "iris";
fileIn  = ""; # fileIn migth be such as "D:/R/library/svc/", if NULL it will work on iris data

fmc = findModelCluster( as.integer(MetOpt), MetLab, KChoice, Nu, q, K, G, Cx, Cy, DName, fileIn); 


[Package svcR version 1.4.2 Index]