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An Accelerated MapReduce-Based K-prototypes for Big Data

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Software Technologies: Applications and Foundations (STAF 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9946))

Abstract

Big data are often characterized by a huge volume and a variety of attributes namely, numerical and categorical. To address this issue, this paper proposes an accelerated MapReduce-based k-prototypes method. The proposed method is based on pruning strategy to accelerate the clustering process by reducing the unnecessary distance computations between cluster centers and data points. Experiments performed on huge synthetic and real data sets show that the proposed method is scalable and improves the efficiency of the existing MapReduce-based k-prototypes method.

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Notes

  1. 1.

    http://hadoop.apache.org/.

  2. 2.

    https://projets.pasteur.fr/projects/rap-r/wiki/SyntheticDataGeneration.

  3. 3.

    https://archive.ics.uci.edu/ml/datasets/KDD+Cup+1999+Data.

  4. 4.

    http://archive.ics.uci.edu/ml/datasets/Covertype.

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Author information

Authors and Affiliations

  1. LARODEC, Université de Tunis, Institut Supérieur de Gestion de Tunis, 41 Avenue de la Liberté, Cité Bouchoucha, 2000, Le Bardo, Tunisia

    Mohamed Aymen Ben HajKacem, Chiheb-Eddine Ben N’cir & Nadia Essoussi

Authors
  1. Mohamed Aymen Ben HajKacem
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  2. Chiheb-Eddine Ben N’cir
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  3. Nadia Essoussi
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Corresponding author

Correspondence to Mohamed Aymen Ben HajKacem .

Editor information

Editors and Affiliations

  1. Dipartimento di Informatica, Universita di Pisa, Pisa, Italy

    Paolo Milazzo

  2. Budapest University of Technology and Economics, Budapest, Hungary

    Dániel Varró

  3. Vienna University of Technology, Vienna, Austria

    Manuel Wimmer

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HajKacem, M.A.B., N’cir, CE.B., Essoussi, N. (2016). An Accelerated MapReduce-Based K-prototypes for Big Data. In: Milazzo, P., Varró, D., Wimmer, M. (eds) Software Technologies: Applications and Foundations. STAF 2016. Lecture Notes in Computer Science(), vol 9946. Springer, Cham. https://doi.org/10.1007/978-3-319-50230-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-50230-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50229-8

  • Online ISBN: 978-3-319-50230-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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