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Scalable Initialization Methods for Large-Scale Clustering

by   Joonas Hämäläinen, et al.
Jyväskylän yliopisto

In this work, 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 utilizes 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. 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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Code Repositories


MATLAB implementation of the M-Spheres Dataset Generator.

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Parallel MATLAB implementations of the K-means clustering methods from the paper Hämäläinen et al. "Scalable Initialization Methods for Large-Scale Clustering".

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