# computeGap2: Gap computation In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

## Description

Estimate the number of clusters thanks to the gap computation.

## Usage

 `1` ```computeGap2(sim, Kmax) ```

## Arguments

 `sim` similarity matrix. `Kmax` maximal number of clusters.

## Details

computeGap2 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```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 <- computeGap2(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 Feb. 4, 2020, 5:08 p.m.