RETRACTED: A Smart Social Insurance Big Data Analytics Framework Based on Machine Learning Algorithms

1. U.S. Census Bureau [USCB]. Social Insurance and Human Services. Section 11. 2004, pp. 355-381.Search in Google Scholar

2. Gantz, J., D. Reinsel. Extracting Value from Chaos, in IDC’s Digital Universe Study. Sponsored by EMC, 2011.Search in Google Scholar

3. Kim, H. Y., J. Cho. Data Governance Framework for Big Data Implementation with NPS Case Analysis in Korea. – Journal of Business and Retail Management Research, Vol. 12, 2018, No 3.10.24052/JBRMR/V12IS03/ART-04Search in Google Scholar

4. Hussain, K., E. Prieto. Big Data in the Finance and Insurance Sectors. Book of New Horizons for a Data-Driven Economy, 2016, pp. 209-223.10.1007/978-3-319-21569-3_12Search in Google Scholar

5. Song, T., S. Ryu. Big Data Analysis Framework for Healthcare and Social Sectors in Korea. – Healthcare Informatics Research, Vol. 21, 2015, No 1, pp. 3-9.10.4258/hir.2015.21.1.3433019625705552Search in Google Scholar

6. Tsai, C. W., C. F. Lai, H. C. Chao, A. V. Vasilakos. Big Data Analytics: A Survey. – Journal of Big Data, Vol. 2, 2015, No 21, pp. 1-32.10.1186/s40537-015-0030-3Search in Google Scholar

7. Bhoola, K., T. Madzhadzhi, J. Narayan, S. Strydom, H. Heerden. Insurance Regulation in Africa: Impact on Insurance and Growth Strategies. Actuarial Society of South Africa’s, Cape Town International Convention Centre, 2014, pp.145-196.Search in Google Scholar

8. Yenkar, V., M. Bartere. Review on Data Mining with Big Data. – International Journal of Computer Science and Mobile Computing, Vol. 3, 2014, Issue 4, pp. 97-102.Search in Google Scholar

9. García, S., S. Ramírez-Gallego, J. Luengo, J. M. Benítez, F. Herrera. Big Data Preprocessing: Methods and Prospects. BMC Big Data Analytics, 2016, pp. 1-22.10.1186/s41044-016-0014-0Search in Google Scholar

10. Tiruveedhula, S., C. M. S. Rani, V. Narayana. A Survey on Clustering Techniques for Big Data Mining. – Indian Journal of Science and Technology, Vol 9, 2016, No 3, pp. 1-12.10.17485/ijst/2016/v9i3/75971Search in Google Scholar

11. Bücker, T. Customer Clustering in the Insurance Sector by Means of Unsupervised Machine Learning. Internship Report, 2016, pp. 1-112.Search in Google Scholar

12. International Labour Organization [ILO]. Social Insurance: Enhancing Social Security Right for Everyone. – Policy Brief, Vol. 3, 2014.Search in Google Scholar

13. Cai, F., N. Le-Khac, T. Kechadi. Clustering Approaches for Financial Data Analysis: A Survey. School of Computer Science & Informatics, 2016.Search in Google Scholar

14. Chahal, H., P. Gulia. Big Data Analytics. – Research Journal of Computer and Information Technology Sciences, Vol. 4, 2016, pp. 1-4.Search in Google Scholar

15. Wang, C., M. Chen, E. Schifano, J. Wu, J. Yan. Statistical Methods and Computing for Big Data. – Statistics and Its Interface, 2016, pp. 399-414.10.4310/SII.2016.v9.n4.a1504159527695593Search in Google Scholar

16. American Academy of Actuaries [AAA]. Big Data and the Role of the Actuary. Big Data Task Force, 2018.Search in Google Scholar

17. International Labor Office [ILO]. ILO Pension Model Technical Guide, 2018.Search in Google Scholar

18. Jadhav, A., D. Pramod, K. Ramanathan. Comparison of Performance of Data Imputation Methods for Numeric Dataset. – International Journal in Applied Artificial Intelligence, 2019, pp. 913-933.10.1080/08839514.2019.1637138Search in Google Scholar

19. García, S., J. Luengo, F. Herrera. Preprocessing in Data Mining. Springer International Publishing, Switzerland, 2015.10.1007/978-3-319-10247-4Search in Google Scholar

20. Aggarwal, C. C. Outlier Analysis. Second Edition. Springer, Cham, 2016.10.1007/978-3-319-47578-3Search in Google Scholar

21. Amorima, R. C., C. Hennigb. Recovering the Number of Clusters in Data Sets with Noise Features Using Feature Rescaling Factors. – Information Sciences Journal, 2016, pp. 1-34.Search in Google Scholar

22. Bridgelall, R. Introduction to Support Vector Machines. Lecture Notes, 2017, pp. 1-18.Search in Google Scholar