dbscan

k-means is the reflex answer, but it quietly assumes round, equal-size clusters and demands you know k upfront. The signal is positioning each alternative by the exact assumption it removes, and knowing when to reach for it.

k-means assumes round, equal-size blobs and a known k. DBSCAN finds arbitrary shapes and outliers but has its own knobs. The hard part is judging clusters without labels. Here is the comparison and the evaluation toolkit.