# PDclust: Probabilistic Distance Clustering In FPDclustering: PD-Clustering and Factor PD-Clustering

## Description

Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant.

## Usage

 `1` ```PDclust(data = NULL, k = 2) ```

## 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

## Value

A 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

## Author(s)

Cristina Tortora and Paul D. McNicholas

## References

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```#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) pdn=PDclust(x,3) plot(x[,1:2],col=pdn\$label) plot(x[,3:4],col=pdn\$label) ```

FPDclustering documentation built on Aug. 24, 2017, 1:05 a.m.