When does DBSCAN beat k-means, and how do you evaluate clusters with no labels?
k-means assumes round, equal-size blobs and a known k. DBSCAN uncovers arbitrary shapes and outliers but brings its own knobs. The tricky part is judging clusters without labels. Here is the comparison and the evaluation toolkit.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
k-means assumes round, equal-size blobs and a known k. DBSCAN uncovers arbitrary shapes and outliers but brings its own knobs. The tricky part is judging clusters without labels. Here is the comparison and the evaluation toolkit.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
