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Homogeneous Vs. Heterogeneous Distributed Data Clustering: A Taxonomy

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Data Management and Analysis

Part of the book series: Studies in Big Data ((SBD,volume 65))

Abstract

Recent advances in computer architecture and networking allow for the opportunity to parallelize the data clustering process. By dividing the problem into smaller partitions, tackling each one in parallel, and then combining the partial solutions, the parallel algorithms can cluster large amounts of data much more efficiently. In specific scenarios, the dataset is inherently distributed over multiple nodes, making it impossible and even infeasible to apply centralized clustering, which has created a need for performing clustering in distributed environments. Distributed clustering solves two problems: infeasibility of collecting data at a central node, due to either technical and/or privacy limitations, and intractability of traditional clustering algorithms on large datasets. In this paper, we provide a novel taxonomy of distributed data clustering algorithms and provide insight into their distributed modeling strategies. The taxonomy classifies the distributed clustering processes as either a homogeneous or heterogeneous process. Various distributed performance and quality measures are also addressed.

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Author information

Authors and Affiliations

  1. Electrical, Computer, and Biomedical Engineering Department, Ryerson University, London, ON, Canada

    Rasha Kashef

  2. Department of Management Science, Ivey Business School, London, ON, Canada

    Marium Warraich

Authors
  1. Rasha Kashef
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  2. Marium Warraich
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Corresponding author

Correspondence to Rasha Kashef .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Department of Computer Engineering Istanbul Medipol University Istanbul, Turkey, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

    Mohammad Moshirpour

  3. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

    Behrouz Far

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Kashef, R., Warraich, M. (2020). Homogeneous Vs. Heterogeneous Distributed Data Clustering: A Taxonomy. In: Alhajj, R., Moshirpour, M., Far, B. (eds) Data Management and Analysis. Studies in Big Data, vol 65. Springer, Cham. https://doi.org/10.1007/978-3-030-32587-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-32587-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32586-2

  • Online ISBN: 978-3-030-32587-9

  • eBook Packages: EngineeringEngineering (R0)

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