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Algorithms for k-median Clustering over Distributed Streams

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Computing and Combinatorics (COCOON 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9797))

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Abstract

We consider the k-median clustering problem over distributed streams. In the distributed streaming setting there are multiple computational nodes where each node receives a data stream and the goal is to maintain an approximation of a function of interest at all time over the union of the local data at all the nodes. The approximation is maintained at a coordinator node which has bidirectional communication channels to all the nodes. This model is also known as the distributed functional monitoring model. A natural variant of this model is the distributed sliding window model where we are interested only in maintaining approximation over a recent period of time.

This paper gives new algorithms for the k-median clustering problem in the distributed streaming model and its sliding-window counter part.

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Notes

  1. 1.

    Throughout this paper with high probability means with probability at least \((1-\frac{1}{n})\).

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Authors and Affiliations

  1. University of Nebraska-Lincoln, Lincoln, NE, 68588, USA

    Sutanu Gayen & N. V. Vinodchandran

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  1. Sutanu Gayen
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  2. N. V. Vinodchandran
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Correspondence to Sutanu Gayen .

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Editors and Affiliations

  1. Virginia Commonwealth Univ , Richmond, Virginia, USA

    Thang N. Dinh

  2. University of Florida , Gainesville, Alabama, USA

    My T. Thai

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Gayen, S., Vinodchandran, N.V. (2016). Algorithms for k-median Clustering over Distributed Streams. In: Dinh, T., Thai, M. (eds) Computing and Combinatorics . COCOON 2016. Lecture Notes in Computer Science(), vol 9797. Springer, Cham. https://doi.org/10.1007/978-3-319-42634-1_43

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  • DOI: https://doi.org/10.1007/978-3-319-42634-1_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42633-4

  • Online ISBN: 978-3-319-42634-1

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