ICGE (version 0.4.2)

INCAindex: INCA index

Description

INCAindex helps to estimate the number of clusters in a dataset.

Usage

INCAindex(d, pert_clus)

Value

Returns an object of class incaix which is a list containing the following components:

well_class

a vector indicating the number of well classified units.

Ni_cluster

a vector indicating each cluster size.

Total

percentage of objects well classified in the partition defined by pert_clus.

Arguments

d

a distance matrix or a dist object with distance information between units.

pert_clus

an n-vector that indicates which group each unit belongs to. Note that the expected values of pert are numbers greater than or equal to 1 (for instance 1,2,3,4..., k). The default value indicates the presence of only one group in data.

Author

Itziar Irigoien ; Konputazio Zientziak eta Adimen Artifiziala, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.

Conchita Arenas ; Departament d'Estadistica, Universitat de Barcelona, Barcelona, Spain.

References

Arenas, C. and Cuadras, C.M. (2002). Some recent statistical methods based on distances. Contributions to Science, 2, 183--191.

Irigoien, I. and Arenas, C. (2008). INCA: New statistic for estimating the number of clusters and identifying atypical units. Statistics in Medicine, 27(15), 2948--2973.

See Also

estW, INCAtest

Examples

#generate 3 clusters, each of them with 20 objects in dimension 5.
mu1 <- sample(1:10, 5, replace=TRUE)
x1 <- matrix(rnorm(20*5, mean = mu1, sd = 1),ncol=5, byrow=TRUE)
mu2 <- sample(1:10, 5, replace=TRUE)
x2 <- matrix(rnorm(20*5, mean = mu2, sd = 1),ncol=5, byrow=TRUE)
mu3 <- sample(1:10, 5, replace=TRUE)
x3 <- matrix(rnorm(20*5, mean = mu3, sd = 1),ncol=5, byrow=TRUE)
x <- rbind(x1,x2,x3)

# Euclidean distance between units.
d <- dist(x)

# given the right partition, calculate the percentage of well classified objects.
partition <- c(rep(1,20), rep(2,20), rep(3,20))
INCAindex(d, partition)


# In order to estimate the number of cluster in data, try several 
#  partitions and compare the results
library(cluster)
T <- rep(NA, 5)
for (l in 2:5){
	part <- pam(d,l)$clustering
	T[l] <- INCAindex(d,part)$Total
}

plot(T, type="b",xlab="Number of clusters", ylab="INCA", xlim=c(1.5, 5.5))