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Probabilistic k-Median Clustering in Data Streams

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Approximation and Online Algorithms (WAOA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7846))

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Abstract

We define the notion of coresets for probabilistic clustering problems and propose the first (k,ε)-coreset constructions for the probabilistic k-median problem in the metric and Euclidean case. The coresets are of size poly(ε − 1,k,log(W/(w min ·p min ·δ))), where W is the expected total weight of the weighted probabilistic input points, w min is the minimum weight of a probabilistic input point, p min is the minimum realization probability, and δ is the error probability of the construction. We show how to maintain our coreset for Euclidean spaces in data streams.

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Author information

Authors and Affiliations

  1. Simon Fraser University, Burnaby, B.C., Canada

    Christiane Lammersen

  2. TU Dortmund, Germany

    Melanie Schmidt & Christian Sohler

Authors
  1. Christiane Lammersen
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  2. Melanie Schmidt
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  3. Christian Sohler
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Leicester, University Road, LE1 7RH, Leicester, UK

    Thomas Erlebach

  2. Dipartimento di Informatica "Renato M. Capocelli", Università di Salerno, Via Ponte Don Melillo, 84081, Fisciano, SA, Italy

    Giuseppe Persiano

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Lammersen, C., Schmidt, M., Sohler, C. (2013). Probabilistic k-Median Clustering in Data Streams. In: Erlebach, T., Persiano, G. (eds) Approximation and Online Algorithms. WAOA 2012. Lecture Notes in Computer Science, vol 7846. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38016-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-38016-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38015-0

  • Online ISBN: 978-3-642-38016-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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