Data Analytics and Data monitoring Based on Database Recommendation - A Comparison

Authors

  • Pooja Mudgil  Assistant Professor, Department of Information Technology, BPIT (GGSIPU), Delhi, New Delhi, India
  • Paras Jain  Department of Information Technology, BPIT (GGSIPU), Delhi, India
  • Vikas Singh  Department of Information Technology, BPIT (GGSIPU), Delhi, India

DOI:

https://doi.org//10.32628/CSEIT1952312

Keywords:

Data Analytics, KDD, java, k-means, Apache Hadoop tool.

Abstract

A comparison of various analysis algorithms based for recommendation systems used in the market and businesses has their usage without considering the fact that if used with correct algorithm can increase its efficiency. Comparison is obtained in this paper after studying and researching various obtained algorithms present currently. This paper presents an introduction to the concept of recommendation systems which are recently working on different domain and then comparison is made, that too employed for scalability, reliability, faster process and efficiency.

References

  1. F.O. Isinkaye, Y. O. Folajimi, B.A. Ojokoh, Recommendation systems: principles, methods, and evaluation, published in Science direct journal of Egyptian Informatcs
  2. P.Resnick, H.R. Varian, Recommender systems published in Commun ACM, 40(3)(1997), pp. 56-58, 10.1145/245108.24512
  3. A.M. Acilar, A. Arslan, A collaborative filtering method based on Artificial Immune Network published in nExp Syst appl, 36(4)(2009), pp. 8324-8332
  4. L.S. Chen, F.H. Hsu, M.C. Chen, Y.C. Hsu, Developing recommender systems with the consideration of product profitability for sellers published in Int. J Inform Sci, 178(4) (2008), pp. 1032-1048
  5. G. Adomavicius, A. Tuzhilin, Toward the next generation of recommender system. A survey of the state-of-the-art and possible extensions, published in IEEE Trans Knowl Data Eng, 17 (6) (2005), pp. 734-749
  6. Mukund Deshpande and George Karypis, Item-Based Top-N Recommendation Algorithms, University of Minnesota, Department of Computer Science Minneapolis, MN 55455
  7. P. Langley, W. Iba, and K. Thompson. An analysis of Bayesian classifiers. In 10th national conference on Artificial Intelligence, pages 223–228. AAAI Press, 1992.
  8. M.J. Pazzani, A framework for collaborative, content-based and demographic filtering, Artific Intell Rev, 13 (1999), pp. 393-408 No. 5(6)
  9. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, VOL. SMC-15, NO. 4, JULY/AUGUST 1985 A Fuzzy ΛΓ-Nearest Neighbor Algorithm JAMES M. KELLER, MICHAEL R. GRAY
  10. G. Murat, G.O. SuleCombination of web page recommender systems, Exp Syst Applicat, 37 (4) (2010), pp. 2911-2922
  11. Isabelle Guyon and Amir Saffari, Compression-Based Averaging of Selective Naive Bayes Classifiers, Journal of Machine Learning Research 8 (2007) 1659-1685
  12. Konstan I, Stathopoulos V, Jose JM. On social networks and collaborative recommendation. In: The proceedings of the 32nd international ACM conference (SIGIR’09), ACM. New York, NY, USA; 2009. p.195–202.
  13. The Optimality of Naive Bayes Harry Zhang Faculty of Computer Science University of New Brunswick Fredericton, New Brunswick, Canada
  14. J.A. Hoeting, D. Madigan, A.E. Raftery, and C.T. Volinsky. Bayesian model averaging: A tutorial. Statistical Science, 14(4):382–417, 1999.
  15. P. Langley, W. Iba, and K. Thompson. An analysis of Bayesian classifiers. In 10th national conference on Artificial Intelligence, pages 223–228. AAAI Press, 1992.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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

How to Cite

[1]
Pooja Mudgil, Paras Jain, Vikas Singh, " Data Analytics and Data monitoring Based on Database Recommendation - A Comparison, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.1166-1170, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952312