Abstract: We present two modifications to the popular k-means clustering algorithm to address the extreme requirements for latency, scalability, and sparsity encountered in user-facing web applications. First, we propose the use of mini-batch optimization for k-means clustering. This reduces computation cost by orders of magnitude compared to the classic batch algorithm while yielding significantly better solutions than online stochastic gradient descent. Second, we achieve sparsity with projected gradient descent, and give a fast ϵaccurate projection onto the L1-ball. Source code is freely available: http://code.google.com/p/sofia-ml
It would be nice to have MiniBatchKMeans, in the same way as it is done in
scikitlearn:MiniBatchKMeans: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html
Paper: https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf
Abstract: We present two modifications to the popular k-means clustering algorithm to address the extreme requirements for latency, scalability, and sparsity encountered in user-facing web applications. First, we propose the use of mini-batch optimization for k-means clustering. This reduces computation cost by orders of magnitude compared to the classic batch algorithm while yielding significantly better solutions than online stochastic gradient descent. Second, we achieve sparsity with projected gradient descent, and give a fast ϵaccurate projection onto the L1-ball. Source code is freely available: http://code.google.com/p/sofia-ml