computeGap: Gap computation

In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

Gap computation

Description

Estimate the number of clusters thanks to the gap computation.

Usage

computeGap(sim, Kmax)

Arguments

sim	similarity matrix.
Kmax	maximal number of clusters.

Details

computeGap returns an estimated number of clusters

Value

The function returns a list containing:

val	vector containing the eigenvalues of the similarity matrix.
gap	vector containing gap values between two successive eigenvalues.
Kmax	estimated number of clusters.

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)
res <- computeGap(sim, Kmax = 20)

plot(res$val[1:20], type = "o", ann = FALSE, axes = FALSE)
abline(v = res$Kmax, col = "darkred")
abline(h = res$val[res$Kmax], col = "darkred")
axis(side = 1, at = c(seq(0,20,by=5), res$Kmax), 
     labels = c(seq(0,20,by=5), res$Kmax), cex.axis = .7)
axis(side = 2)
title("Automatic estimation of number of clusters - Gap method")
mtext("Number of clusters", side = 1, line = 3)
mtext("Eigenvalue", side = 2, line = 3)
box()

RclusTool documentation built on May 29, 2024, 5:23 a.m.