fpc (version 2.2-9)

neginc: Neg-entropy normality index for cluster validation

Description

Cluster validity index based on the neg-entropy distances of within-cluster distributions to normal distribution, see Lago-Fernandez and Corbacho (2010).

Usage

neginc(x,clustering)

Arguments

x

something that can be coerced into a numerical matrix. Euclidean dataset.

clustering

vector of integers with length =nrow(x); indicating the cluster for each observation.

Value

Index value, see Lago-Fernandez and Corbacho (2010). The lower (i.e., the more negative) the better.

References

Lago-Fernandez, L. F. and Corbacho, F. (2010) Normality-based validation for crisp clustering. Pattern Recognition 43, 782-795.

Examples

# NOT RUN {
  options(digits=3)
  iriss <- as.matrix(iris[c(1:10,51:55,101:105),-5])
  irisc <- as.numeric(iris[c(1:10,51:55,101:105),5])
  neginc(iriss,irisc)
# }