k-means concept from scratch (under 10 minutes): a standard CMake project demonstrating a naive k-means implementation

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k-means undoubtedly has varying levels of complexity, depending on approach and application. However, the core concept boils down to 5 easy to grasp fundamental step which can be put together in under ~10 minutes in CPP.

Usage:

CMake the project (thats pretty much it!).

The target and helper scripts are auto-run post the cmake build process.

The helper script graph.py graphs data before and after clustering

before clustering	after clustering

Gist:

step 1: select number of clusters

step 2: assign random initial centroids

step 3: form clusters (i.e., assign each point to its nearest centroid)

step 4: compute new cluster centroids

step 5: reform clusters (i.e, re-assign each point to its nearest centroid)