fda.usc (version 1.2.3)

kmeans.fd: K-Means Clustering for functional data

Description

Perform k-means clustering on functional data.

Usage

kmeans.fd(fdataobj,ncl=2,metric=metric.lp,dfunc=func.trim.FM,
max.iter=100,par.metric=NULL,par.dfunc=list(trim=0.05),
par.ini=list(method="sample"),draw=TRUE,...)
kmeans.center.ini(fdataobj,ncl=2,metric=metric.lp,
draw=TRUE,method="sample",iter=100,par.metric=NULL,...)

Arguments

fdataobj

fdata class object.

ncl

See details section.

metric

Metric function, by default metric.lp.

dfunc

Type of depth measure, by default FM depth.

max.iter

Maximum number of iterations for the detection of centers.

draw

=TRUE, draw the curves in the color of the centers.

par.dfunc

List of arguments to pass to the dfunc function .

par.ini

List of arguments to pass to the kmeans.center.ini function .

method

Method for selecting initial centers. If method="Sample" (by default) takes n times a random selection by the ncl centers. The ncl curves with greater distance are the initial centers. If

iter

Maximum number of random samples for the initial selection of centers.

par.metric

List of arguments to pass to the metric function.

...

Further arguments passed to or from other methods.

Value

• Return:
• cluster
  Indexes of groups assigned.
• centers
  Curves centers.

Details

The method searches the locations around which are grouped data (for a predetermined number of groups). If ncl=NULL, randomizes the initial centers, ncl=2 using kmeans.center.ini function. If ncl is an integer, indicating the number of groups to classify, are selected ncl initial centers using kmeans.center.ini function. If ncl is a vector of integers, indicating the position of the initial centers with length(ncl) equal to number of groups. If ncl is a fdata class objecct, ncl are the initial centers curves with nrow(ncl) number of groups.

References

Hartigan, J. A. and Wong, M. A. (1979). A K{-}means clustering algorithm. Applied Statistics 28, 100 -108.

See Also

See Also generic kmeans function.

Examples

data(phoneme)
mlearn<-phoneme$learn[c(1:50,101:150,201:250),]

#Unsupervised classification
out.fd1=kmeans.fd(mlearn,ncl=3,draw=TRUE)
out.fd2=kmeans.fd(mlearn,ncl=3,draw=TRUE,par.ini=list(method="exact"))
# Different Depth function
ind=c(17,77,126)
out.fd3=kmeans.fd(mlearn,ncl=mlearn[ind,],draw=FALSE,
dfunc=func.trim.FM,par.dfunc=list(trim=0.1))
out.fd4=kmeans.fd(mlearn,ncl=mlearn[ind,],draw=FALSE,
dfunc=func.med.FM)
out.fd5=kmeans.fd(mlearn,ncl=3,dfunc=func.trim.RPD,
max.iter=10,par.dfunc=list(dfunc="depth.FM",deriv=c(0,1,1)))
group=c(rep(1,50),rep(2,50),rep(3,50))
table(out.fd5$cluster,group)