Improving Scalable K-Means++
Hämäläinen, J., Kärkkäinen, T., & Rossi, T. (2021). Improving Scalable K-Means++. Algorithms, 14(1), Article 6. https://doi.org/10.3390/a14010006
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Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces produced by the random projection method for the initialization. The proposed methods are scalable and can be run in parallel, which make them suitable for initializing large-scale problems. In the experiments, comparison of the proposed methods to the K-means++ and K-means‖ methods is conducted using an extensive set of reference and synthetic large-scale datasets. Concerning the latter, a novel high-dimensional clustering data generation algorithm is given. The experiments show that the proposed methods compare favorably to the state-of-the-art by improving clustering accuracy and the speed of convergence. We also observe that the currently most popular K-means++ initialization behaves like the random one in the very high-dimensional cases ...
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Related funder(s)Academy of Finland
Funding program(s)Academy Programme, AoF; Research profiles, AoF
Additional information about fundingThe work has been supported by the Academy of Finland from the projects 311877 (Demo) and 315550 (HNP-AI).
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