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"[Kmeans] algorithm has a loose relationship to the k-nearest neighbor classifier, a popular machine learning technique for classification that is often confused with k-means due to the name." Maybe an ML expert could elaborate? I've been curious on my own projects when to actually mention when they use true ML or not.

https://en.wikipedia.org/wiki/K-means_clustering

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Right. See my comment above.

As to why it was in the project: If you treat each pixel as an example vector in 3 dimensions and cluster, you get the “dominant” colors for the image. It’s a primitive way to compress images as well. In this case I just was using it to generat fun cards that would use a minimal number of dominant colors. It’s still in the code if you’d like to use it but a bit hidden.

K-nearest neighbor clusters based upon the k most similar objects.

K-means, clusters on centroids that are means. After every interation new means are calculated and then reclustering occurs.

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