Improving Scalable K-Means

• Published in: Algorithms Vol. 14; no. 1; p. 6
• Main Authors: Hämäläinen, Joonas, Kärkkäinen, Tommi, Rossi, Tuomo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: Algorithms; Cluster analysis; Clustering; clustering initialization; Datasets; Feature selection; K-means; Methods; Principal components analysis; Prototypes; random projection; Subspaces; Vector quantization
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a14010006

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.