GPDC: Gaussian PD-Clustering

In FPDclustering: PD-Clustering and Factor PD-Clustering

Gaussian PD-Clustering

Description

An implementation of Gaussian PD-Clustering GPDC, an extention of PD-clustering adjusted for cluster size that uses a dissimilarity measure based on the Gaussian density.

Usage

GPDC(data=NULL,k=2,method="kmedoids", nr=5,iter=100)

Arguments

data	A matrix or data frame such that rows correspond to observations and columns correspond to variables.
k	A numerical parameter giving the number of clusters
method	A parameter that selects center starts. Options available are random ("random"), kmedoid ("kmedoid", by default), and PDC ("PDclust").
nr	Number of random starts when method set to "random"
iter	Maximum number of iterations

Value

A class FPDclustering list with components

label	A vector of integers indicating the cluster membership for each unit
centers	A matrix of cluster means
sigma	A list of K elements, with the variance-covariance matrix per cluster
probability	A matrix of probability of each point belonging to each cluster
JDF	The value of the Joint distance function
iter	The number of iterations
data	the data set

Author(s)

Cristina Tortora and Francesco Palumbo

References

Tortora C., McNicholas P.D., and Palumbo F. A probabilistic distance clustering algorithm using Gaussian and Student-t multivariate density distributions. SN Computer Science, 1:65, 2020.

C. Rainey, C. Tortora and F.Palumbo. A parametric version of probabilistic distance clustering. In: Greselin F., Deldossi L., Bagnato L., Vichi M. (eds) Statistical Learning of Complex Data. CLADAG 2017. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham, 33-43 2019. doi.org/10.1007/978-3-030-21140-0_4

See Also

PDC,PDQ

Examples

#Load the data
data(ais)
dataSEL=ais[,c(10,3,5,8)]

#Clustering
res=GPDC(dataSEL,k=2,method = "kmedoids")

#Results
table(res$label,ais$sex)
plot(res)
summary(res)

FPDclustering documentation built on Aug. 31, 2022, 5:09 p.m.