Labelling {svcR}R Documentation

Computation of clustering model by support vector machine

Description

Labelling methods try to identify clusters in a grid.

Usage

## S4 method for signature 'findModelCluster':
Labelling.compute(x,       
                MetLab          = 1,    
                MatriceKernel   = MK,   
                MatriceK        = M_K,  
                pp              = vpp,  
                Nu              = 1,         
                G               = 1,         
                q               = 1,         
                ncol            = 1,         
                nlin            = 1,         
                RadiusC         = 2,        
                r               = 2,         
                KernChoice      = 0.01,         
                NbClassInData   = 0.01  )

## S4 method for signature 'findModelCluster':
AdjacencyPP(x, MatriceKernel=matrix(), Vec1=vector(), Vec2=vector())

## S4 method for signature 'findModelCluster':
Adjacency(x, MatriceKernel=matrix())

## S4 method for signature 'findModelCluster':
MST_labelling(x, MatriceKernel=matrix())

## S4 method for signature 'findModelCluster':
KNN_labelling(x, MatriceKernel=matrix())

Arguments

x a findModelCluster object
MetLab option taking value 1 (grid labelling) or 2 (mst labelling) or 3 (knn labelling)
MatriceKernel kernel matrix with vector format
MatriceK kernel matrix with vector format
pp option taking value 0 (Euclidian) or 1 (RBF) or 2 (Exponential)
Nu kernel parameter
G kernel parameter
q kernel parameter
ncol number of attributes
nlin size of variables
RadiusC model radius
r residual radius
KernChoice kernel id
NbClassInData number of classes in data
Vec1 vector for computing adjacency
Vec2 vector for computing adjacency

Value

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

ClassPoints class of grid points
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

fmc = findModelCluster.Test(); 

fmc@NumPoints; # values of the grid


[Package svcR version 1.4.2 Index]