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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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Correspondence to Rasha Kashef .

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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

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