#93: K-means clustering: machine learning algorithm to easily split observations into multiple buckets

K-means clustering is an algorithm for partitioning data into

multiple, non-overlapping buckets. For example, if you have a bunch of

points in two-dimensional space, this algorithm can easily find

concentrated clusters of points. To be honest, that’s quite a simple

task for humans. Just plot all the points on a piece of paper and find

areas with higher density. For example, most of the points are located

on the top-left of the plane, some at the bottom and a few at the

centre-right. However, this is not that straightforward once you can no

longer rely on graphical representation. For instance, when your data

points live 3-, 4- or 100-dimensional space. Turns out, this is not that

uncommon. Let me clarify.

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