enter image description here

This is from running kmeans clustering with k on the x-axis (ranging from 2 to 10) and the silhouette distance on the y-axis.

Clearly there's peaks at k=3, k=4 and it seems to decline from there. It doesn't resemble an elbow and thought it should rise as k gets larger (due to over fitting on he training set). Do I just lack data?

I'm computing the silhouette distance using a 80-20 train test split.

  • $\begingroup$ So, what’s the size of your data? $\endgroup$
    – pythinker
    Apr 8, 2019 at 20:46
  • $\begingroup$ few thousand rows , TFIDF based clustering ~ 50 000 features $\endgroup$
    – MrL
    Apr 8, 2019 at 21:59

1 Answer 1


First of all, you do have two elbows: one at $k=4$ and a large one at $k=8$. The second isn't very apparent because you haven't drawn out the plot for larger values of $k$. If you do you might get a figure like this:

Secondly, you aren't meant to look for an elbow when computing the silhouette score! The silhouette score accounts for both inter- and intra-cluster distance, as such it can be used for selecting $k$ on its own (i.e. select the $k$ that produces the best silhouette score).

Note: I'm not familiar with the "silhouette distance", I assume it is somewhat related to the silhouette score (maybe its inverse).

The "elbow" criterion should be used when dealing with metrics that tend to improve as $k$ increases (e.g. inertia).


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