The simplest thing would be to use PCA to reduce the dimensionality of your data to 2 or 3 dimensions. k-means clustering ought to assign a group to each row of your data so you can easily plot the different groups on the reduced dataset. Here’s a simple way to do PCA though you could … Read more
The first line does the cluster analysis, and stores the cluster labels in a component called cluster_iris$cluster which is just a vector of numbers. The second line puts that cluster number as a categorical label onto the rows of the original data set. So now your iris data has all the petal and sepal stuff … Read more
If you are interested in the optimisation result from NbClust, you will find it in nc$Best.partition where each number is the cluster number for respective row in the data matrix. for example > nc$Best.partition [1] 1 2 3 4 5 1 3 5 1 1 4 1 4 1 5 1 5 1 4 2 … Read more
It won’t be k-means anymore. K-means is variance minimization, and it seems your objective is to produce paritions of a predefined size, not of minimum variance. However, here is a tutorial that shows how to modify k-means to produce clusters of the same size. You can easily extend this to produce clusters of the desired … Read more