findModelCluster {svcR} | R Documentation |
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.
findModelCluster(MetOpt="", MetLab="", KernChoice="", Nu="", q="", K="", G="", Cx="", Cy="", DName="", fileIn="")
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) |
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 |
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
no return
Nicolas Turenne - INRA France nicolas.turenne@jouy.inra.fr
N.Turenne , Some Heuristics to speed-up Support Vector Clustering , technical report 2006, INRA, France http://migale.jouy.inra.fr/~turenne/svc.pdf
## exemple with iris data MetOpt = 1; # optimisation method with randomization MetLab = 1; # grid labelling KChoice = 1; # 0: eucli 1: radial 2: radial+dist Nu = 0.7; q = 1200; # 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 findModelCluster(MetOpt, MetLab, KChoice, Nu, q, K, G, Cx, Cy, DName, fileIn);