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#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.
Read more: https://nurkiewicz.com/93
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By Tomasz NurkiewiczK-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.
Read more: https://nurkiewicz.com/93
Get the new episode straight to your mailbox: https://nurkiewicz.com/newsletter