A fast k-means algorithm based on multi-granularity

The k-means algorithm has been widely used since it was proposed, but the standard k-means algorithm does not perform well in terms of efficiency when dealing with large-scale data. To solve this problem, in this paper, we propose a fast kmeans algorithm based on multiple granularities. First, from the coarse-grained perspective, we use the clustering distribution information to narrow the search range of sample points, which makes the proposed algorithm very advantageous on large k. Second, from the fine-grained perspective, we use the rules of upper and lower bounds to reduce the number of sample points involved in the distance calculation, thus reducing many unnecessary distance calculations. Finally, we evaluate the proposed k-means algorithm on several real-world datasets, and the experimental results show that the proposed algorithm converges hundreds of times faster than standard k-means on average with the accuracy loss controlled at about three percent, and the speedup of the algorithm is more obvious when the dataset size is larger and the dimensionality of the dataset is higher.