Description Usage Arguments Value Author(s) See Also Examples
Assigns labels to data points according to cluster membership, when the clusters are defined as high density regions
1 2 |
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". |
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.
Jussi Klemela
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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)
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