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Parallel Implementation of Classification Algorithms Based on MapReduce

  • Qing He
  • Fuzhen Zhuang
  • Jincheng Li
  • Zhongzhi Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6401)

Abstract

Data mining has attracted extensive research for several decades. As an important task of data mining, classification plays an important role in information retrieval, web searching, CRM, etc. Most of the present classification techniques are serial, which become impractical for large dataset. The computing resource is under-utilized and the executing time is not waitable. Provided the program mode of MapReduce, we propose the parallel implementation methods of several classification algorithms, such as k-nearest neighbors, naive bayesian model and decision tree, etc. Preparatory experiments show that the proposed parallel methods can not only process large dataset, but also can be extended to execute on a cluster, which can significantly improve the efficiency.

Keywords

Data Mining Classification Parallel Implementation Large Dataset MapReduce 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Qing He
    • 1
  • Fuzhen Zhuang
    • 1
    • 2
  • Jincheng Li
    • 1
    • 2
  • Zhongzhi Shi
    • 1
  1. 1.The Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina

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