delt (version 0.8.2)

cluster.lst: Assigns labels to data points

Description

Assigns labels to data points according to cluster membership, when the clusters are defined as high density regions

Usage

cluster.lst(dendat, h, N = NULL, cut = NULL, lambda = NULL, complete = FALSE,
type = "grid", labels = "number", nodes = NULL, minobs = 1)

Arguments

dendat

n*d matrix of real numbers; the data matrix.

h

positive real number; smoothing parameter of a kernel density estimator

N

d vector of positive integers; a kernel estimate is evaluated on a regular grid which is such that in direction i there are N[i] points; N is needed only when type="grid".

cut

real number between 0 and 1; this parameter is used to determine the level "lambda" of the level set whose disconnected components determine the clusters.

lambda

positive real number between; "lambda" is the level of the level set whose disconnected components determine the clusters.

complete

TRUE or FALSE; if complete=FALSE, then partial clustering is performed, otherwise complete clustering is performed.

type

either "grid" or "adaptive"; if type="grid", then the density is estimated using a discretized kernel estimator with a regular grid; otherwise the density is estimated using a discretized kernel estimator with an adaptive grid.

labels

if labels="number", then the cluster labels are integers 1,2,..., otherwise the cluster labels are colors.

nodes

a vector of positive integers; contains pointers to the nodes of a level set tree; the nodes indicate which disconnected components of level sets define the clusters.

minobs

a positive integer; this is a parameter of function "pcf.greedy.kernel".

Value

a vector of cluster labels; the vector has length equal to the number of rows of the data matrix "dendat". The cluster labels are either numbers or names of colors.

See Also

pcf.greedy.kernel

Examples

library(denpro)
# generate data
seed<-1
n<-50
d<-2
l<-3; D<-4; c<-D/sqrt(2)
M<-matrix(0,l,d); M[2,]<-c; M[3,]<--c
sig<-matrix(1,l,d)
p<-rep(1/l,l)
dendat<-sim.data(type="mixt",n=n,M=M,sig=sig,p=p,seed=seed)

# partial clustering with a fixed level 
h<-(4/(d+2))^(1/(d+4))*n^(-1/(d+4))*apply(dendat,2,sd)
N<-rep(20,d)
cl<-cluster.lst(dendat,h,N=N,labels="colors",type="grid",lambda=0.02)
#plot(dendat,col=cl)

# complete clustering with a fixed level
cl<-cluster.lst(dendat,h,N=N,complete=TRUE,labels="colors",type="grid",lambda=0.02)
#plot(dendat,col=cl)

# complete clustering with locally changing levels
N<-rep(20,d)
pcf<-pcf.kern(dendat,h,N)
lst<-leafsfirst(pcf)
nodes<-findbnodes(lst,modenum=3)
cl<-cluster.lst(dendat,h,N,nodes=nodes,complete=TRUE,labels="colors")
#plot(dendat,col=cl)