Effective Machine Learning Techniques used in Big Data Analytics

Authors

  • S. Senthil Kumar Assistant Professor, Department of Commerce with Computer Applications, Dr.SNS Rajalakshmi College of Arts and Science (Autonomous), Coimbatore, Tamil Nadu, India
  • V. Kathiresan Head, Department of Computer Applications (PG), Dr.SNS Rajalakshmi College of Arts and Science (Autonomous), Coimbatore, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajsat-2017.6.1.942

Keywords:

Big data, Feature Selection, Supervised Learning, Unsupervised Learning, Deep Learning

Abstract

Big data is a general term for massive amount of digital data being collected from various sources that are too large and raw in form. Big data deals with new challenges like complexity, security, risks to privacy. Big data is redefining the data management from extraction, transformation and processing to cleaning and reducing [1]. There has been a lot of growth in the amount of data generated by web these days. The data has been so large that it becomes difficult to analyse it with the help of our traditional mining methods. Big data term has been coined for data that exceeds the processing capability [2]. Moreover, the rising data volume has contributed to the increasing demand for big data analytics.

References

G.-H. Kim, S. Trimi, and J.-H. Chung, "Big-Data Applications in the Government Sector," Communications of the ACM, vol. 57, no. 3, pp. 78-75, 2014.

R. Gupta, S. Gupta, and A. Singhal, "Big Data: Overview," International Journal of Computer Trends and Technology (IJCTT), vol. 9, no. 5, pp. March 2014.

L. Kaufman and P. J. Rousseeuw, "Finding groups in data. an introduction to cluster analysis," Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics, vol. 1, New York: Wiley, 1990.

T. Zhang, R. Ramakrishnan, and M. Livny, "Birch: an efficient data clustering method for very large databases," in ACM SIGMOD Record, vol. 25, no. 2, ACM, 1996, pp. 103–114.

T. B¨ack, "Evolutionary computation: Toward a new philosophy of machine intelligence," 1997.

C. M. Bishop et al., "Pattern recognition and machine learning," Springer New York, vol. 4, no. 4, 2006.

D. K. Bhattacharyya and J. K. Kalita, "Network anomaly detection: A machine learning perspective," CRC Press, 2013.

N. Djuric, "Big data algorithms for visualization and supervised learning," Ph.D. dissertation, Temple University, 2014.

C.-J. Hsieh, S. Si, and I. S. Dhillon, "A divide-and-conquer solver for kernel support vector machines," arXiv preprint arXiv:1311.0914, 2013.

H. Ahmed, P. Mahanta, D. Bhattacharyya, J. Kalita, and A. Ghosh, "Intersected coexpressed subcube miner: An effective triclustering algorithm," in Information and Communication Technologies (WICT), 2011 World Congress on, IEEE, 2011, pp. 846–851.

F. Hoppner, "Fuzzy cluster analysis: methods for classification, data analysis and image recognition," John Wiley & Sons, 1999.

G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504–507, 2006.

Downloads

Published

27-03-2017

How to Cite

Senthil Kumar, S., & Kathiresan, V. (2017). Effective Machine Learning Techniques used in Big Data Analytics. Asian Journal of Science and Applied Technology, 6(1), 18–21. https://doi.org/10.51983/ajsat-2017.6.1.942

Issue

Vol. 6 No. 1 (2017): January-June 2017

Section

Articles

License

Copyright (c) 2017 The Research Publication

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.