Introduction to K-Means Clustering
One thing not mentioned with k-means is it requires your data to be in Euclidean space. You can't just come up with a distance function and do clustering with it. That's one reason the other techniques often make sense. In fact, those techniques often don't care what the data actually is, they only need the distances, and even then they don't need all of them.<p>Still, k-means is enormously practical despite its shortcomings.