Parallel K-prototypes for Clustering Big Data

  • Mohamed Aymen Ben HajKacem
  • Chiheb-Eddine Ben N’cir
  • Nadia Essoussi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9330)

Abstract

Big data clustering has become an important challenge in data mining. Indeed, Big data are often characterized by a huge volume and a variety of attributes namely, numerical and categorical. To deal with these challenges, we propose the parallel k-prototypes method which is based on the Map-Reduce model. This method is able to perform efficient groupings on large-scale and mixed type of data. Experiments realized on huge data sets show the performance of the proposed method in clustering large-scale of mixed data.

Keywords

Big data K-prototypes Map-reduce Mixed data 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mohamed Aymen Ben HajKacem
    • 1
  • Chiheb-Eddine Ben N’cir
    • 1
  • Nadia Essoussi
    • 1
  1. 1.LARODECUniversité de Tunis, Institut Supérieur de Gestion de TunisLe BardoTunisia

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