cluster.lst: Assigns labels to data points

In delt: Estimation of Multivariate Densities Using Adaptive Partitions

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.

Author(s)

Jussi Klemela

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)

delt documentation built on May 2, 2019, 3:42 p.m.