Show notes
K-means clustering is an algorithm for partitioning data into
multiple, non-overlapping buckets. For example, if you have a bunch ofpoints in two-dimensional space, this algorithm can easily findconcentrated clusters of points. To be honest, that’s quite a simpletask for humans. Just plot all the points on a piece of paper and findareas with higher density. For example, most of the points are locatedon the top-left of the plane, some at the bottom and a few at thecentre-right. However, this is not that straightforward once you can nolonger rely on graphical representation. For instance, when your datapoints live 3-, 4- or 100-dimensional space. Turns out, this is not thatuncommon. Let me clarify.