FPDclustering (version 2.3.1)

PDC: Probabilistic Distance Clustering

Description

Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD clustering is based on the constraint that the product of the probability and the distance of each point to any cluster centre is a constant.

Usage

PDC(data = NULL, k = 2)

Value

A class FPDclustering list with components

label

A vector of integers indicating the cluster membership for each unit

centers

A matrix of cluster centers

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

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

Author

Cristina Tortora and Paul D. McNicholas

References

Ben-Israel C. and Iyigun C. Probabilistic D-Clustering. Journal of Classification, 25(1), 5-26, 2008.

Examples

#Normally generated clusters
c1 = c(+2,+2,2,2)
c2 = c(-2,-2,-2,-2)
c3 = c(-3,3,-3,3)
n=200
x1 = cbind(rnorm(n, c1[1]), rnorm(n, c1[2]), rnorm(n, c1[3]), rnorm(n, c1[4]) )
x2 = cbind(rnorm(n, c2[1]), rnorm(n, c2[2]),rnorm(n, c2[3]), rnorm(n, c2[4]) )
x3 = cbind(rnorm(n, c3[1]), rnorm(n, c3[2]),rnorm(n, c3[3]), rnorm(n, c3[4]) )
x = rbind(x1,x2,x3)

#Clustering
pdn=PDC(x,3)

#Results
plot(pdn)