Asked about Finding structure without labels

You have clustered your customers into five groups. How do you know the groups are real?

An answer that works

I would generate random data with the same shape and range and cluster it the same way. k-means returns k tidy groups from uniform noise, and both the elbow and the silhouette will show a peak on that noise, so the only meaningful comparison is whether my data scores clearly better than the null. After that I would name each cluster from its members, because a cluster nobody can describe in a sentence is not a finding.

Then they push further

Is checking stability across random seeds enough?

No. k-means on uniform random data partitions stably too, because the partition is a property of the region's geometry rather than of structure in the points. Stability rules out one failure and not the one that matters.

What gets marked up

Marked up for comparing against a null rather than reading a score, and for knowing that stability does not distinguish real structure from none.

Reading an answer is not being able to give one. The section that teaches this is Finding structure without labels, 5 hours of Unsupervised learning.

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