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