The Study on Predictive Analysis Algorithm : Survey

Authors(2) :-Anandajayam P, Dr. N. Sivakumar

Nowadays the increase of data variety considered very controversy problem. So inventive methods are mandatory for analytics especially in big data where the data are very complex, structured, unstructured and semi structured. It is owing to a good deal of research which is carried out in Predictive, Prescriptive, Diagnostic, and Descriptive. Because of the increase in the huge volume of data this paper helps the researcher in analysing the prediction. Machine learning is one of the materialize ways to fabricate the analytic model for machines to learn from data and able to do analysis on prediction. The cue “big data analytics” can be simplified by the subsequent four manners: data, problem, methodology, and technology. In this paper, we discuss the study of predictive analytics. Predictive analytics is a prerequisite approach that handles the necessary quantum of potentially fragile data to predict the future possibilities, trends, and measures. Predictive analytics are composed of various mathematical and meticulous methods used to produce a new technique to predict future possibilities. This paper, scrutinizes about various predictive analytics algorithms with for and against in big data. The predictive algorithms have been explained in upcoming parts.

Authors and Affiliations

Anandajayam P
Research Scholar, Department of Computer Science and Engineering/ Pondicherry Engineering College, Tamil Nadu, India
Dr. N. Sivakumar
Assistant Professor, Department of Computer Science and Engineering/ Pondicherry Engineering College, Tamil Nadu, India

Big Data Analytics, Predictive Analysis, Machine Learning Algorithm

  1. V.Kavya and S.Arumugam. A Review On Predictive Analytics In Data Mining. International Journal of Chaos, Control, Modelling and Simulation Vol.5, No.1/2/3, September 2016
  2. Lidong Wang and Cheryl Ann Alexander, Machine Learning in Big Data. International Journal of Mathematical, Engineering and Management Sciences Vol. 1, No. 2, 5261, ISSN: 2455-774, 2016.
  3. R.J. Hyndman and S.Fan, Density forecasting for long-term peak electricity demand, IEEE Trans. Power Syst, PP.1142-1153, May 2010.
  4. F. Lei and P. Hu, A baseline model for office building energy consumption in hot summer and cold winter region ,Proceedings of International Conference on Management and Service Science. PP.1-4, May 2009.
  5. G. Kumaresan and P. Rajakumar Predictive Analytics Using Big Data: A Survey, International Journal of Management, Information Technology and Engineering.ISSN:2348-0513; ISSN (Online): 2454-471X; Vol. 3, Issue 9, PP.61-68, Sep 2015.
  6. Jiang Zheng, Aldo Dagnino, An Initial Study of Predictive Machine Learning Analytics on Large Volumes of Historical Data for Power System Applications, IEEE International Conference on Big Data PP. 978-1-4799, Vol .1, 2014.
  7. M.D. Anto Praveena1 Dr. B. Bharathi2, A Survey Paper on Big Data Analytics,.
  8. Sunpreet Kaur and Sonika Jindal, A Survey on Machine Learning Algorithms, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2763 Issue 11, Volume 3 (November 2016).
  9. S.Nagaparameshwara Chary, A Survey on Comparative Analysis of Decision Tree Algorithms in Data Mining International Conference on Innovative Applications in Engineering and Information Technology (ICIAEIT), Volume.3,Special Issue.1,March.2017.
  10. Dipak V.Patil and R.S Bichkar, Issues in Optimization of Decision Tree Learning: A Survey International Journal of Applied Information System-ISSN:2249-0868 Foundation of Computer Science FCS ,Volume.3-5,July,2012.
  11. Mr.Brija in R Patel and Mr.Kushik K Rana, A Survey on Decision Tree Algorithm For Classification International Journal of Engineering Development and Research, Volume 2, Issue 1, ISSN: 2321-9939, 2014.
  12. Marina Komaroff and Noven Pharmaceuticals Multinomial Logistic Regression Models”, PharmaSUG International Journal of Engineering Development and Research, Volume 2, Issue 1, ISSN: 2341-9239, 2017.
  13. Ashis Pradhan Support Vector Machine -A Survey International Journal of Emerging Technology and Advanced Engineering ,Volume 2, ISSN 2250-2459,August 2012.
  16. Ashis Pradhan Luckyson Khaidem Predicting the direction of stock market prices using random forest, to appear in Applied Mathematical Finance, 2016.
  18. introduction-random-fores-simplified
  19. Susan Walsh Binary Logistic Regression What, When, and How JMP Discovery Conference 2016.
  20. TR v2 4139%5b1%5d.pdf
  21. Sofia Strombergsson Binary Logistic Regression and its application to data from a study of children’s recognition of their own recorded voices, Term paper in Statistical Methods, Spring 2009.
  22. Sidhakam Bhattacharyya Comparative Analysis using Multinomial Logistic Regression International Conference on Business and Information Management (ICBIM), IEEE, 2014.
  23. Erkan ARI and Zeki YILDIZ Parallel Lines Assumption In Ordinal Logistic Regression And Analysis Approaches, International Interdisciplinary Journal of Scientific Research Volume 3, Dec 2014.
  24. Swati Gupta A Regression Modeling Technique on Data Mining, International Journal of Computer Applications, ISSN: 0975-8887, Volume 116-No.9, April 2015, Dec 2014.
  25. 202.
  26. S.V.Suryanarayana, A Survey: Spectral Clustering Applications and its Enhancements. International Journal of Computer Science and Information Technologies Vol. 6, No.185-189, ISSN: 0975-9646,2015.
  27. Cuimei Guo and Sheng Zheng, A Survey on Spectral Clustering. Proceedings of 2010 Conference on Dependable Computing (CDC2010) ,November 20-22, 2010.
  28. J. Han, M. Kamber, and J. Pei, Data mining: concepts and techniques: Morgan kaufmann. 2006.
  29. S. Ding, F. Wu, J. Qian, H. Jia, and F. Jin, Research on data stream clustering algorithms. Artificial Intelligence Review, pp. 1-8, 2013
  30. H. Yang, D. Yi, and C. Yu, Cluster Data Streams with Noisy Variables. Communications in Statistics-Simulation and Computation, 2014.
  31. C.C. Aggarwal and P. S. Yu, A framework for clustering uncertain data streams. In Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on, 2008, pp. 150- 159.
  32. G. Sheikholeslami, S. Chatterjee, and A. Zhang, WaveCluster: a wavelet based clustering approach for spatial data in very large databases. The VLDB Journal, vol. 8, pp. 289-304, 2000.
  33. W.-K. Loh and Y.-H. Park, A Survey on Density-Based Clustering Algorithms. in Ubiquitous Information Technologies and Applications, ed: Springer, pp. 775-780, 2014
  34. Alexander J. Stimpson and Mary L. Cummings, Assessing Intervention Timing in Computer-Based Education Using Machine Learning Algorithms. ScienceDirect, 2017.
  35. Eric T. Bradlow and Manish Gangwar, The Role of Big Data and Predictive Analytics in Retailing. ScienceDirect, 2017.
  36. Teng Li and Jian Tang, Performance Modeling and Predictive Scheduling for Distributed Stream Data Processing. IEEE,2016.
  37. Kavya.V, Arumugam.S, A Review On Predictive Analytics In DataMining. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS), 2016.
  38. Nishchol Mishra and Dr.Sanjay Silakari, Predictive Analytics: A Survey, Trends, Applications, Oppurtunities & Challenges. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (3), 4434- 4438, ISSN: 0975-9646, 2012.
  39. Panagiotis D.Diamantoulaki, Big Data Analytics for Dynamic Energy Management in Smart Grids. ScienceDirect, 2015.
  40. G. Kumaresan,P. Rajakumar, Predictive Analytics Using Big Data: A Survey. IJMITE, 2015.
  41. Tobias Schoenherr and Cheri Speier-Pero, Data Science, Predictive Analytics, and Big Data in Supply Chain Management. Journal of Business Logistics, 2015.
  42. Xing He, A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory. IEEE, 2015.
  43. Vimalkumar K, A Big Data Framework for Intrusion Detection in Smart Grids Using Apache Spark. IEEE, 2017.
  44. Lidong Wang, Cheryl Ann Alexander, Machine Learning in Big Data. International Journal of Mathematical, Engineering and Management Sciences, 2016.
  45. Hina Gulati, Predictive Analytics Using Data Mining Technique, 2nd International Conference on Computing for Sustainable Global Development, New Delhi, 2015, pp. 713-716.,2015

Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 677-686
Manuscript Number : CSEIT1831147
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Anandajayam P, Dr. N. Sivakumar, "The Study on Predictive Analysis Algorithm : Survey ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.677-686, January-February-2018.
Journal URL :

Article Preview