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Fast and accurate mining of correlated heavy hitters

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Abstract

The problem of mining correlated heavy hitters (CHH) from a two-dimensional data stream has been introduced recently, and a deterministic algorithm based on the use of the Misra–Gries algorithm has been proposed by Lahiri et al. to solve it. In this paper we present a new counter-based algorithm for tracking CHHs, formally prove its error bounds and correctness and show, through extensive experimental results, that our algorithm outperforms the Misra–Gries based algorithm with regard to accuracy and speed whilst requiring asymptotically much less space.

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Notes

  1. http://ita.ee.lbl.gov/html/contrib/WorldCup.html

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Authors and Affiliations

  1. University of Salento, Lecce, Italy

    Italo Epicoco, Massimo Cafaro & Marco Pulimeno

  2. Euro-Mediterranean Center on Climate Change, CMCC Foundation, Lecce, Italy

    Italo Epicoco & Massimo Cafaro

Authors
  1. Italo Epicoco
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  2. Massimo Cafaro
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  3. Marco Pulimeno
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Corresponding author

Correspondence to Massimo Cafaro.

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Responsible editor: Toon Calders.

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Epicoco, I., Cafaro, M. & Pulimeno, M. Fast and accurate mining of correlated heavy hitters. Data Min Knowl Disc 32, 162–186 (2018). https://doi.org/10.1007/s10618-017-0526-x

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