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)


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.


Data Mining Classification Parallel Implementation Large Dataset MapReduce 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)Google Scholar
  2. 2.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1997)Google Scholar
  3. 3.
    Weiss, S.M., Kulikowski, C.A.: Computer Systems that Learn: Classification and Prediction Methods from Statistics. In: Neural Nets, Machine Learning, and Expert Systems. Morgan Kaufmann, San Francisco (1991)Google Scholar
  4. 4.
    Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine learning, neural and statistical classification. Ellis Horwood, New York (1994)zbMATHGoogle Scholar
  5. 5.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  6. 6.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley and Sons, Chichester (2001)zbMATHGoogle Scholar
  7. 7.
    Cover, T., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Information Theory 13, 21–27 (1967)zbMATHCrossRefGoogle Scholar
  8. 8.
    Dasarathy, B.V.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techiniques. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
  9. 9.
  10. 10.
    Weiss, A.: Computing in the Clouds. netWorker 11(4), 16–25 (2007)CrossRefGoogle Scholar
  11. 11.
    Buyya, R., Yeo, C.S., Venugopal, S.: Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities. In: Proc. of the 10th IEEE International Conference on High Performance Computing and Communications, China (2008)Google Scholar
  12. 12.
    Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. In: Proc. of the 6th Symposium on Operating System Design and Implementation, USA (2004)Google Scholar
  13. 13.
  14. 14.
  15. 15.
    Elsayed, T., Jimmy, L., Douglas, W.O.: Pairwise Document Similarity in Large Collections with MapReduce. In: Proc. of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies, pp. 262–268 (2008)Google Scholar

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

Personalised recommendations