An approach to achieve high efficiency for large volume data processing using multiple clustering algorithms

  • Authors

    • Sarada. B
    • Vinayaka Murthy. M
    • Udaya Rani. V
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.25059
  • Big Data, Canopy Clustering, Hadoop, K-Mean Clustering, Data Processing Techniques, Mapreduce, Rk Sorting Algorithm.

  • Abstract

    Now a days data is increasing exponentially daily in terms of velocity, variety and volume which is also known as Big data. When the dataset has small number of dimensions, limited number of clusters and less number of data points the existing traditional clustering al- gorithms will give the expected results. As we know this is the Big Data age, with large volume of data sets through the traditional clus- tering algorithms we will not be able to get expected results. So there is a need to develop a new approach which gives better accuracy and computational time for large volume of data processing. The Proposed new System Architecture is a combination of canopy, Kmeans and RK sorting algorithm through Map Reduce Hadoop frame work platform. The analysis shows that the large volume of data processing will take less computational time and higher accuracy, and the RK sorting does not require swapping of elements and stack spaces.

     

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

      [9] 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.

      [10] 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.

      [11] “A Study of DM Techniques for CRM†at National Women’s Science Congress, SB Arts & KCP Science College, Bijapur, on November 7 th – 9 th, 2008, Lilavathi -1, PP 65-74.

      [12] “E – Governance Data analysis by Data Mining Algorithm†at the National Conference on E –Governance and its Application –alma nac ‘08’ at Dayananda Sagar Institutions Bangalore – 78, on Nov.27 th – 28 th ,2008, PP -45.

      [13] “Illustrations of k-means algorithm “,at UGC sponsored State level seminar on New Frontiers in the Development of science and technology,16 th &17th April 2009, BMS college for women -2009.

      [14] Ramakrishna,Assistant Professor Department of Computer Science, Reva Institute of Science and Management, Email: dcrrama@yahoo.co.in “New RK Sorting Algorithmâ€.

  • Downloads

  • How to Cite

    B, S., Murthy. M, V., & Rani. V, U. (2018). An approach to achieve high efficiency for large volume data processing using multiple clustering algorithms. International Journal of Engineering & Technology, 7(4.5), 689-692. https://doi.org/10.14419/ijet.v7i4.5.25059

    Received date: 2018-12-30

    Accepted date: 2018-12-30

    Published date: 2018-09-22