Interview: you cluster and 3 clusters look 'right', but 2 mix categories — what next?
hardAnswer
- (1) Feature analysis: what's shared in each mixed cluster (maybe a real behavior spans your labels).
- (2) Try higher k → often 'mixed' clusters split cleanly.
- (3) Try alternate algorithm (HDBSCAN, GMM) — mixed clusters may be low-density boundary.
- (4) Feature engineering: add features that separate the mixed classes.
- (5) Semi-supervised: use partial labels to nudge clustering via constrained k-means / seeded k-means.
- (6) Accept that categories don't fully align with unsupervised structure and communicate to stakeholders.
Check yourself — multiple choice
- Nothing
- Analyze shared features + try higher k + alternate algorithm + feature-eng + semi-sup / constrained k-means; also accept unsup ≠ label taxonomy
- Random
- Just retrain
Mixed clusters: higher k / HDBSCAN / new features / semi-sup / accept.
#interview#applications#clustering
Practise Unsupervised Learning
214 interview questions in this topic.
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