<p>The first clustering algorithm you will learn about is <a href="https://en.wikipedia.org/wiki/K-means_clustering" rel="nofollow noreferrer">k-means</a>. That is nice and good, but unfortunately people will sometimes think that k-means is the One Tool to solve all their clustering problems and neglect finding out about drawbacks and alternatives to k-means.</p>

<p>In such cases, I find both <a href="https://stats.stackexchange.com/a/133841/1352">Anony-Mousse's</a> and <a href="https://stats.stackexchange.com/a/133694/1352">David Robinson's</a> answers to the question <a href="https://stats.stackexchange.com/q/133656/1352">How to understand the drawbacks of K-means</a> invaluable. Both answers are crafted with love, with lots of wonderful graphics, and I find myself recommending this question and both answers over and over. Both definitely qualify as "answers I wish I could show everyone."</p>

<p>[First of two bounties begun 9 Feb 2016. 2nd bounty started 15 Feb. Now awarded.]</p>
