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Unsupervised Learning

Clustering, dimensionality reduction, density estimation and anomaly detection.

easyclustering

How does k-means work and what are its main limitations?

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Interview-style answer

$Initialize k centroids (k-means++ is best).$
$Assign each point to the nearest centroid, then recompute centroids as the mean of assigned points; repeat until convergence.$
$Limitations: you must choose k, it assumes spherical clusters of similar size, it's sensitive to scale and outliers, and it can get stuck in local minima (mitigated by multiple restarts).$

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