Scalable Initialization Methods for Large-Scale Clustering

• URL: http://arxiv.org/abs/2007.11937v1
• Date: Thu, 23 Jul 2020 11:29:53 GMT
• Title: Scalable Initialization Methods for Large-Scale Clustering
• Authors: Joonas H\"am\"al\"ainen, Tommi K\"arkk\"ainen, Tuomo Rossi
• Abstract summary: Two new methods for K-means clustering are proposed. The proposed methods are scalable and can be run in parallel. Experiments show that the proposed methods compare favorably to the state-of-the-art.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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