Great questions! I can answer a few of them. - Yes, you can get stick in a local maximum. However, see the k-means++ algorithm for a clever initialization sc

Great questions! I can answer a few of them. - Yes, you can get stick in a local maximum. However, see the k-means++ algorithm for a clever initialization scheme that gets you within a good constant factor of the global maximum. - The best way to pick K is to fit the algorithm with a range of K and see which K seems to give the best application performance. - It is "your job" in creating the feature space to ensure that feature-space nearness corresponds to application-space nearness. This is not easy at all, and essentially the only reliable test if you've done a good job is if the final algorithm performs as you'd like it to. I think that the post is really observing that, once you understand it, ML algorithms aren't really that complex. However, once you understand it, most things aren't that complex, and that doesn't make fully understanding them easy in the first place.