Exact Acceleration of K-Means++ and K-Means
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K-Means++ and its distributed variant K-Means have become de facto tools for selecting the initial seeds of K-means. While alternatives have been developed, the effectiveness, ease of implementation, and theoretical grounding of the K-means++ and methods have made them difficult to "best" from a holistic perspective. By considering the limited opportunities within seed selection to perform pruning, we develop specialized triangle inequality pruning strategies and a dynamic priority queue to show the first acceleration of K-Means++ and K-Means that is faster in run-time while being algorithmicly equivalent. For both algorithms we are able to reduce distance computations by over 500×. For K-means++ this results in up to a 17× speedup in run-time and a 551× speedup for K-means. We achieve this with simple, but carefully chosen, modifications to known techniques which makes it easy to integrate our approach into existing implementations of these algorithms.
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