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Australasian Joint Conference on Artificial Intelligence

AI 2019: AI 2019: Advances in Artificial Intelligence pp 191–202Cite as

Online K-Means Clustering with Lightweight Coresets

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11919))

Abstract

Coresets are representative samples of data that can be used to train machine learning models with provable guarantees of approximating the accuracy of training on the full data set. They have been used for scalable clustering of large datasets and result in better cluster partitions compared to clustering a random sample. In this paper, we present a novel approach of constructing lightweight coresets on subsets of data that can fit in memory while performing a streaming variant of k-means clustering known as online k-means. Experimental results show that this approach generates cluster partitions of comparable accuracy to the regular online k-means algorithm in less time, or superior partitions in comparable time.

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Authors and Affiliations

  1. School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

    Jia Shun Low, Zahra Ghafoori & Christopher Leckie

Authors
  1. Jia Shun Low
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  2. Zahra Ghafoori
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  3. Christopher Leckie
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Corresponding author

Correspondence to Jia Shun Low .

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Editors and Affiliations

  1. University of South Australia, Adelaide, SA, Australia

    Jixue Liu

  2. The University of Melbourne, Melbourne, VIC, Australia

    James Bailey

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Low, J.S., Ghafoori, Z., Leckie, C. (2019). Online K-Means Clustering with Lightweight Coresets. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-35288-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35287-5

  • Online ISBN: 978-3-030-35288-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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