A review on Data Mining & Big Data Analytics

  • Authors

    • Yashasree Tummala
    • Dr. Hemantha Kumar Kalluri
  • The time of enormous information is presently progressing. Be that as it may, the customary information investigation will most likely be unable to wrench such huge amounts of information. The inquiry that emerges now is, the way to build up an elite stage to effectively examine huge information and how to plan a suitable mining calculation to locate the helpful things from enormous information. To profoundly talk about this issue, this paper starts with a concise prologue to information investigation, trailed by the exchanges of enormous information examination.

  • References

    1. [1] Lyman P, Varian H, How much information 2003? Tech. Rep, (2004

      [2] Xu R, Wunsch D. Clustering. Hoboken: Wiley-IEEE Press; (2009).

      [3] 3. Ding C, He X, K-means clustering via principal component analysis, In: Proceedings of the Twenty-first International Conference on Machine Learning, (2004), pp 1–9.

      [4] Kollios G, Gunopulos D, Koudas N, Berchtold S, Efficient biased sampling for approximate clustering and outlier detection in large data sets, IEEE Trans Knowl Data Eng. (2013);15(5), pp 1134–40.

      [5] Press G, $16.1 billion big data market: 2014 predictions from IDC and IIA, Forbes, Tech. Rep. 2013

      [6] Han J, Data mining: concepts and techniques, San Francisco: Morgan Kaufmann Publishers Inc. 2005.

      [7] Agrawal R, Imieliński T, Swami A, Mining association rules between sets of items in large databases, Proc ACM SIGMOD Int Conf Manag Data. (1993);22(2):207–16.

      [8] Witten IH, Frank E, Data mining: practical machine learning tools and techniques, Morgan Kaufmann Publishers Inc.; 2005.

      [9] Abbass H, Newton C, Sarker R, Data mining: a heuristic approach, Hershey: IGI Global; (2012).

      [10] Cannataro M, Congiusta A, Pugliese A, Talia D, Trunfio P, Distributed data mining on grids: services, tools, and applications, IEEE Trans Syst Man Cyber Part B Cyber. 2014;34(6): pp. 2451–65.

      [11] McQueen JB, Some methods of classification and analysis of multivariate observations, In: Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, pp 251–231.

      [12] Safavian S, Landgrebe D, A survey of decision tree classifier methodology. IEEE Trans Syst Man Cyber. (1991);21(3):660–74.

      [13] McCallum A, Nigam K, A comparison of event models for naive bayes text classification. In: Proceedings of the National Conference on Artificial Intelligence,. pp. 41–48.

      [14] Katal A, Wazid M, Goudar R, Big data: issues, challenges, tools and good practices, In: Proceedings of the International Conference on Contemporary Computing, (2014). pp 404–409.

      [15] Baraniuk RG, More is less: signal processing and the data deluge, Science. (2011);298(6018):357–9.

      [16] Chunxia Zhang, Ming Yang, Jing Lv, Wanqi Yang, An improved hybrid collaborative filtering algorithm based on tags and timefactor- IEEE Explore(2018)

      [17] Yan Yang, Hao Wang, Multi-view Clustering: A survey- IEEE Explore (2018) 83-107

      [18] 18.Rahil Sharma, Suely Oliveira- Community Detection Algorithm for Big Social Networks using Hybrid Architecture – ScienceDirect 2017

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  • How to Cite

    Tummala, Y., & Kalluri, D. H. K. (2018). A review on Data Mining & Big Data Analytics. International Journal of Engineering & Technology, 7(4.24), 92-94. https://doi.org/10.14419/ijet.v7i4.24.21863