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

findModelCluster(MetOpt="", MetLab="", Nu="", q="", K="", G="", Cx="", Cy="", DName="", fileIn="")

Arguments

MetOpt option taking value 1 (randomization) or 2 (quadratic programming)
MetLab option taking value 1 (grid labelling) or 2 (mst labelling) or 3 (knn labelling)
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

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

no return

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
Nu      = 0.5; 
q       = 40;   # lot of clusters
K       = 1;    # only 1  nearest neighbour for clustering
Cx = Cy = 0; # we use principal component analysis factors
G       = 15; # 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

findModelCluster(MetOpt, MetLab, Nu, q, K, G, Cx, Cy, DName, fileIn); 


[Package svcR version 1.1 Index]