Asked about Clustering, hands on

How do you choose the number of clusters?

An answer that works

Try a range and compare internal scores such as silhouette and the elbow in within-cluster variance, then check stability across seeds and subsamples, and above all whether the clusters are interpretable and useful for the purpose. There is rarely one correct k.

Then they push further

When would k-means be the wrong choice?

When clusters are elongated or irregular, very different in size or density, or when there are many outliers. Density-based methods such as HDBSCAN, or Gaussian mixtures for elliptical clusters, fit those better.

Reading an answer is not being able to give one. The section that teaches this is Clustering, hands on, 4 hours of Unsupervised learning.

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