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Fitting Data on a Grain of Rice

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Algorithmic Aspects of Cloud Computing (ALGOCLOUD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14053))

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Abstract

Coresets are among the most successful compression paradigms. For clustering, a coreset B of a point set A preserves the clustering cost for any candidate solution C. In general, we are interested in finding a B that is as small as possible. In this overview, we will survey techniques for constructing coresets for clustering problems, their applications, and potential future directions.

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Notes

  1. 1.

    Faster algorithms are possible, but not discussed here.

  2. 2.

    \(\tilde{O}(x)\) hides terms \(\text {polylog}(x)\).

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Acknowledgements

The author acknowledges the support of the Independent Research Fund Denmark (DFF) under a Sapere Aude Research Leader grant No. 1051-00106B.

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  1. Aarhus University, Aarhus, Denmark

    Chris Schwiegelshohn

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Correspondence to Chris Schwiegelshohn .

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  1. Sapienza University of Rome, Rome, Italy

    Ioannis Chatzigiannakis

  2. Ionian University, Corfu, Greece

    Ioannis Karydis

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Schwiegelshohn, C. (2024). Fitting Data on a Grain of Rice. In: Chatzigiannakis, I., Karydis, I. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2023. Lecture Notes in Computer Science, vol 14053. Springer, Cham. https://doi.org/10.1007/978-3-031-49361-4_13

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