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A Comparative Study of the Some Methods Used in Constructing Coresets for Clustering Large Datasets

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Abstract

Coresets can be described as a compact subset such that models trained on coresets will also provide a good fit with models trained on full data set. Using coresets, we can scale down a big data to a tiny one to reduce the computational cost of a machine learning problem. In recent years, data scientists have investigated various methods to create coresets with many techniques and approaches, especially for solving the problem of clustering large datasets. In this paper, we make comparisons among four state-of-the-art algorithms: ProTraS by Ros and Guillaume with improvements, Lightweight Coreset by Bachem et al. Adaptive Sampling Coreset by Feldman et al. and a native Farthest-First-Traversal-based coreset construction. We briefly introduce these four algorithms and compare their performances to find out the benefits and drawbacks of each one.

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Acknowledgements

This research is funded by a project with the Department of Science and Technology, Ho Chi Minh City, Vietnam (contract with HCMUT No. 42/2019/HD-QPTKHCN, dated 11/7/2019). We would like to thank the FDSE 2019 organizing committee and paper reviewers for suggesting corrections and improvements. The audience at our presentation session in FDSE 2019 conference also offered constructive feedbacks for the paper.

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Authors and Affiliations

  1. Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam

    Nguyen Le Hoang, Le Hong Trang & Tran Khanh Dang

  2. Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Nguyen Le Hoang, Le Hong Trang & Tran Khanh Dang

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  1. Nguyen Le Hoang
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  2. Le Hong Trang
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  3. Tran Khanh Dang
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Correspondence to Tran Khanh Dang.

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This article is part of the topical collection “Future Data and Security Engineering 2019” guest edited by Tran Khanh Dang.

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Le Hoang, N., Trang, L.H. & Dang, T.K. A Comparative Study of the Some Methods Used in Constructing Coresets for Clustering Large Datasets. SN COMPUT. SCI. 1, 215 (2020). https://doi.org/10.1007/s42979-020-00227-7

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