What is K-means clustering? (Plus free Python code)

A universe of clusters

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What do you thinK it means?

This post is about k-means clustering. I’ll describe what it isn’t, what it is, and I’ll give some examples of the process. Don’t worry – it’s actually pretty simple.

Broadly – K-means clustering is just the following steps;

  1. Choose a certain number of groups (we refer to this number as K, hence K-means)
  2. Randomly group our data into that number of groups
  3. Measure how badly we did (as in – how different the different points in our groups are)
  4. Repeat hundreds or millions of times
  5. Choose the attempt that was the least bad.

If that’s all you really needed – crack on to that Dime cake, otherwise – I’ll go into more detail below.

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