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- W4285280693 abstract "The k-means algorithm is a widely used clustering algorithm, but the time overhead of the algorithm is relatively high on large-scale data sets and high-dimensional data sets. In this paper, we propose an efficient heuristic algorithm with the main idea of narrowing the search space of sample points and reducing the number of sample points reallocated during the iteration. Experimental results show that our algorithm has excellent time performance in most of the datasets, and the clustering quality is almost the same or even better than that of the exact k-means algorithm." @default.
- W4285280693 created "2022-07-14" @default.
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- W4285280693 date "2022-01-01" @default.
- W4285280693 modified "2023-10-14" @default.
- W4285280693 title "A Fast Heuristic k-means Algorithm Based on Nearest Neighbor Information" @default.
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- W4285280693 doi "https://doi.org/10.1007/978-981-19-2448-4_11" @default.
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