A1 Journal article (refereed)
Improving Scalable K-Means++ (2021)


Hämäläinen, J., Kärkkäinen, T., & Rossi, T. (2021). Improving Scalable K-Means++. Algorithms, 14(1), Article 6. https://doi.org/10.3390/a14010006


JYU authors or editors


Publication details

All authors or editorsHämäläinen, Joonas; Kärkkäinen, Tommi; Rossi, Tuomo

Journal or seriesAlgorithms

eISSN1999-4893

Publication year2021

Publication date27/12/2020

Volume14

Issue number1

Article number6

PublisherMDPI AG

Publication countrySwitzerland

Publication languageEnglish

DOIhttps://doi.org/10.3390/a14010006

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/73628


Abstract

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


Keywordsdata miningcluster analysisalgorithmicsalgorithms

Free keywordsclustering initialization; K-means‖; K-means++; random projection


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Ministry reportingYes

Reporting Year2021

JUFO rating1


Last updated on 2024-03-04 at 19:57