critMNCut: Multiple Normalized Cut
In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

critMNCut

R Documentation

Multiple Normalized Cut

Description

Calculate the Multiple Normalized Cut value from a similarity matrix.

Usage

critMNCut(sim, label)

Arguments

`sim`	similarity matrix.
`label`	vector of labels.

Details

critMNCut returns the Multiple Normalized Cut value

Value

MNCut Multiple Normalized Cut value.

Examples

dat <- rbind(matrix(rnorm(100, mean = 0, sd = 0.3), ncol = 2), 
           matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2), 
           matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2))
           
sim <- computeGaussianSimilarity(dat, 1)

ML <- list(c("10","20"),  c("60","70"))
CNL <- list(c("30","80"), c("90","120"))

res <- KwaySSSC(sim, K=0, list.ML=ML, list.CNL=CNL)

critMNCut(sim, res$label)

RclusTool documentation built on Aug. 29, 2022, 9:07 a.m.