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- W4308469945 abstract "The k -means is the most popular clustering algorithm, but, as it needs too many distance computations, its speed is dramatically fall down against high-dimensional data. Although, there are some quite fast variants proposed in literature, but, there is still much room for improvement against high-dimensional large-scale datasets. What proposed here, k-means-g*, is based on a simple geometric concept. For four distinct points, if distance between all pairs except one pair are known, then, a lower bound can be determined for the unknown distance. Utilizing this technique in the assignment step of the k -means, many high-dimensional distance computations can be easily ignored, where small amount of memory is used. Both theoretical and experimental results approves speed of the k-means-g* against recently published fast variants. For over than 50 cases, out of 70 cases of performed experiments, it is faster than other algorithms. The C++ source code for k-means-g* is publicly available." @default.
- W4308469945 created "2022-11-12" @default.
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- W4308469945 date "2022-12-01" @default.
- W4308469945 modified "2023-09-26" @default.
- W4308469945 title "K-means-G*: Accelerating k-means clustering algorithm utilizing primitive geometric concepts" @default.
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- W4308469945 doi "https://doi.org/10.1016/j.ins.2022.11.001" @default.
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