Analysis of large volume data processing using clustering algorithms

  • Authors

    • Sarada. B
    • Vinayaka Murthy. M
    • Udaya Rani. V
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.25058
  • Big Data, Canopy Clustering, Hadoop, K-Mean Clustering, Data Processing Techniques, Mapreduce.

  • The study of large dataset with velocity, variety and volume which is also known as Big data. When the dataset has limited number of clusters, low dimensions and small number of data points the existing traditional clustering algorithms can be used.. As we know this is the internet age, the data is growing very fast and existing clustering algorithms are not giving the acceptable results in terms of time complexity and spatial complexity. So there is a need to develop a new approach of applying clustering of large volume of data processing with low time and spatial complexity through MapReduce and Hadoop frame work applying to different clustering algorithms, k-means, Canopy clustering and proposed algorithm .The analysis shows that the large volume of data processing will take low time and spatial complexity when compared to small volume of data.

     

     


     

  • References

    1. [1] Ambika.s and Kavitha.G,†Overcoming the Defects of K-means clustering by using Canopy Clustering Algorithm IJSRD |Vol. 4, Issue 05, 2016 | ISSN (online): 2321-0613.

      [2] D. Napoleon & P. Ganga lakshmi “An Efficient K-Means Clustering Algorithm for Reducing Time Complexity Using Uniform Distribution Data Points†IEEE, 2010, pp, 42-45.

      [3] Dweepna Garg 1, Khushboo Trivedi 2, B.B.Panchal ,†A Comparative study of Clustering Algorithms using MapReduce 2321-0613 in Hadoop†IJSRDt| Vol. 4, Issue 05,2016 | ISSN (online):

      [4] M. S. Chen, J. Han, and P. S. Yu. IEEE Trans Knowledge and Data Engineering Data mining. An overview from a database perspective, 8:866-883, 1996.

      [5] Ayman E. Kheer, Ahmed I. El Seddawy, Amira M. Idrees,†Performance Tuning of K-Mean Clustering Algorithm a Step towards Efficient DSSâ€, IJIRCST,ISSN: 2347-5552, Volume 2, Issue 6, November – 2014.

      [6] A. Hunter and S. Parsons, "A review of uncertainty handling formalisms", Applications of Uncertainty Formalisms LNAI 1455, pp.8-37. Springer – Verlag, 1998.

      [7] H.R. Shashidhar, G.T. Raju and M Vinayaka Murthy,“Efficient Estimation of Result Selectivity for Web Query Optimi zationâ€, International Journal of Pure and Applied Mathematics, Volume 17 No. 7 2017, PP 193-205, ISSN:311-8080.

      [8] H.R. Shashidhar, G.T. Raju and M Vinayaka Murthy, “Effective Cost Models for Web Query Optimizationâ€, International Journal of Pure and.

      [9] Applied Mathematics, Volume 117 No. 20, 17, PP 727-739, ISSN: 1311-8080.

      [10] M Vinayaka Murthy “Survey On Web Query Optimization Trends and Future Researchâ€, International Conference On Advanced Material Technology 2016, Issue –V, pp 409 – 417, Elsevier Materials Today: Proceedings.

      [11] M Vinayaka Murthy, “A Comparative Study on Mining the & Healthy Food Preferences of Women Clustersâ€, Journal of Scientific Engineering Research, Vol 6, Issue 7, pp 126 131, 2017, ISSN: 2229-5518.

  • Downloads

  • How to Cite

    B, S., Murthy. M, V., & Rani. V, U. (2018). Analysis of large volume data processing using clustering algorithms. International Journal of Engineering & Technology, 7(4.5), 685-688. https://doi.org/10.14419/ijet.v7i4.5.25058