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Clustering Data Streams

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Data Stream Management

Part of the book series: Data-Centric Systems and Applications ((DCSA))

Abstract

Clustering is a useful and ubiquitous tool in data analysis. Broadly speaking, clustering is the problem of grouping a data set into several groups such that, under some definition of “similarity,” similar items are in the same group and dissimilar items are in different groups. In this chapter we focus on clustering in a streaming scenario where a small number of data items are presented at a time and we cannot store all the data points. Thus, our algorithms are restricted to a single pass. The space restriction is typically sublinear, \(o(n)\), where the number of input points is \(n\).

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Author information

Authors and Affiliations

  1. Department of Computer Information Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Sudipto Guha

  2. Computer Science Department, Stanford University, Stanford, CA, 94305, USA

    Nina Mishra

Authors
  1. Sudipto Guha
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  2. Nina Mishra
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Correspondence to Sudipto Guha .

Editor information

Editors and Affiliations

  1. University Campus - Kounoupidiana, School of ECE, Techn. Univ. of Crete University Campus - Kounoupidiana, Chania, Greece

    Minos Garofalakis

  2. Microsoft Corporation, Redmond, Washington, USA

    Johannes Gehrke

  3. Amazon India , Bangalore, India

    Rajeev Rastogi

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Guha, S., Mishra, N. (2016). Clustering Data Streams. In: Garofalakis, M., Gehrke, J., Rastogi, R. (eds) Data Stream Management. Data-Centric Systems and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28608-0_8

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  • DOI: https://doi.org/10.1007/978-3-540-28608-0_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28607-3

  • Online ISBN: 978-3-540-28608-0

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