For analysis of your clusters you can use the silhouette coefficient or silhouette width. These are available in
factoextra package in R.
I will explain what basically in silhouette analysis:
The silhouette coefficient is calculated as follows:
1) For each observation i, it calculates the average dissimilarity between i and all the other points within the same cluster which i belongs. Let’s call this average dissimilarity “Di”.
2) Now we do the same dissimilarity calculation between i and all the other clusters and get the lowest value among them. That is, we find the dissimilarity between i and the cluster that is closest to i right after its own cluster. Let’s call that value “Ci”
3) The silhouette (Si) width is the difference between Ci and Di (Ci — Di) divided by the greatest of those two values (max(Di, Ci)).
Si = (Ci — Di) / max(Di, Ci)
So, the interpretation of the silhouette width is the following:
- Si > 0 means that the observation is well clustered. The closest it is to 1, the best it is clustered.
- Si < 0 means that the observation was placed in the wrong cluster.
- Si = 0 means that the observation is between two clusters.
Basic code to calculate the above and visualise:
sil <- silhouette(clustering$cluster, dist(input))