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A Functional Approach to Parallelizing Data Mining Algorithms in Java

  • Ivan KholodEmail author
  • Andrey Shorov
  • Sergei Gorlatch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10421)

Abstract

We describe a new approach to parallelizing data mining algorithms. We use the representation of an algorithm as a sequence of functions and we use higher-order functions to express parallel execution. Our approach generalizes the popular MapReduce programming model by enabling not only data-parallel, but also task-parallel implementation and a combination of both. We implement our approach as an extension of the industrial-strength library Xelopes, and we illustrate it by developing a multi-threaded Java program for the 1R classification algorithm, with experiments on a multi-core processor.

Keywords

Parallel algorithms Data mining Parallel data mining Multithreads Multi-core processors MapReduce, homomorphisms 

Notes

Acknowledgments

This work was supported by the Ministry of Education and Science of the Russian Federation in the framework of the state order “Organization of Scientific Research”, task #2.6113.2017/BУ, and by the German Research Agency (DFG) in the framework of the Cluster of Excellence Cells-in-Motion at the University of Muenster.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Saint Petersburg Electrotechnical University “LETI”Saint PetersburgRussia
  2. 2.University of MuensterMuensterGermany

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