An Effective and Efficient Clustering Based on K-Means Using MapReduce and TLBO

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)

Abstract

A plethora of clustering methods were developed since time unknown, but these methods have failed to prove that they are flawlessly efficient and also to give an optimized result in the field it might be that, parallel programming technique like MapReduce and evolutionary methods of computation address solutions to this issue as well. We use this limitation as an advantage to combine a new efficient method for optimization, ‘Teaching Learning based Optimization (TLBO)’ and a new parallel programing technique called MapReduce to develop a new approach to provide good quality clusters. In this paper, teaching learning based optimization is collaborated along with Parallel K-means Using MapReduce. Firstly, it makes K-means with MapReduce to work with massive amount of data and after that it takes the advantage of global search ability of TLBO to provide a global optimal result.

Keywords

K-means clustering TLBO optimization Hadoop MapReduce 

References

  1. 1.
    Sharma, S., ShikhaRai: Genetic K-means algorithm—implementation and analysis. Int. J. Recent Technol. Eng. (IJRTE) 1(2), (June 2012). ISSN:2277-3878Google Scholar
  2. 2.
    Zhang, J.: A parallel clustering algorithm with MPI—MKmeans. J. Comput. 8(1), (January 2013)Google Scholar
  3. 3.
    Mummareddy, P.K., Satapathy, S.C.: An hybrid approach for data clustering using K-means and teaching learning based optimization. In: Satapathy, S.C., et al. (eds.) Emerging ICT for Bridging the Future, vol. 2, p. 165. Springer International Publishing, Switzerland (2015). Adv. Intell. Syst. Comput, 338. doi:  10.1007/978-3-319-13731-5_19
  4. 4.
    Zhao, W., Ma, H., He, Q.: Parallel K-means clustering based on mapreduce. Springer, Berlin Heidelberg (2009)Google Scholar
  5. 5.
    Fahim Ahmed, M.: Parallel implementation of K-means on multi-core processors. GESJ: Comput. Sci. Telecommun. (2014)Google Scholar
  6. 6.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Google White Paper to Appear in OSDI (2004)Google Scholar
  7. 7.
    MapReduce [OL]: http:// en.wikipedia.org/wiki/MapReduceGoogle Scholar
  8. 8.
  9. 9.
  10. 10.
    Satapathy, S.C., Naik, A., Parvathi, K.: A teaching learning based optimization based on orthogonal design for solving global optimization problems. SpringerPlus 2, 130 (2013)Google Scholar
  11. 11.
    White, T., Hadoop: The Definitive Guide. O’reilly Media (June 2009)Google Scholar
  12. 12.

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Anil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia

Personalised recommendations