Machine Learning with R Cookbook - Second Edition

How to do it...

Perform the following steps to find the optimum number of clusters for the k-means clustering:

1. First, calculate the within sum of squares (withinss) of different numbers of clusters:

        > nk = 2:10
        > set.seed(22)
        > WSS = sapply(nk, function(k) {
        +     kmeans(customer, centers=k)$tot.withinss
        + })
        > WSS
        Output
        [1] 123.49224  88.07028  61.34890  48.76431  47.20813
        [6]  45.48114  29.58014  28.87519  23.21331

2. You can then use a line plot to plot the within sum of squares with a different number of k:

        > plot(nk, WSS, type="l", xlab= "number of k", ylab="within sum of        squares")  

The line plot of the within sum of squares with regard to the ...