unsupervised

EM is the classic latent-variable algorithm, and a GMM is how it appears in practice. What interviewers reward is the E-step/M-step alternation, why it is soft clustering where k-means is hard, and the honest caveat that it only reaches a local optimum. Here is the answer.

k-means is the reflex answer, yet it silently assumes round, equal-size clusters and requires you to know k in advance. What earns credit is positioning each alternative by the specific assumption it drops, and knowing when to use it.

A Gaussian mixture trained by EM has a well-known trap: one Gaussian shrinks onto a single point and the likelihood races to infinity. Understanding the cause, plus the three standard remedies, tells apart people who merely ran sklearn from those who grasp it.

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.