Advertisement

Big Data Analysis: Theory and Applications

  • Yong ShiEmail author
  • Pei Quan
Conference paper
  • 55 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11958)

Abstract

With the continuous improvement of data processing capabilities and storage capabilities, Big Data Era has entered the public sight. Under such a circumstance, the generation of massive data has greatly facilitated the development of data mining algorithms. This paper describes the status of data mining and presents three of our works: optimization-based data mining, intelligent knowledge and the intelligence quotient of Artificial Intelligence respectively. Besides that, we also introduced some applications that have emerged in the context of big data. Furthermore, this paper indicates three potential directions for future research of big data analysis.

Keywords

Big data Data mining Optimization-based data mining Intelligent knowledge 

Notes

Acknowledgement

This work was partially supported by the National Natural Science Foundation of China [Grant No. 7193000078, No. 91546201, No. 71331005, No. 71110107026, No. 11671379, No. 11331012] and UCAS Grant [No. Y55202LY00].

References

  1. 1.
    Cios, K.J., Moore, G.W.: Uniqueness of medical data mining. Artif. Intell. Med. 26(1–2), 1–24 (2002)CrossRefGoogle Scholar
  2. 2.
    Freed, N., Glover, F.: Simple but powerful goal programming models for discriminant problems. Eur. J. Oper. Res. 7(1), 44–60 (1981)CrossRefGoogle Scholar
  3. 3.
    He, J., Liu, X., Shi, Y., Xu, W., Yan, N.: Classifications of credit cardholder behavior by using fuzzy linear programming. Int. J. Inf. Technol. Decis. Making 3(04), 633–650 (2004)CrossRefGoogle Scholar
  4. 4.
    Kou, G.: Multi-class multi-criteria mathematical programming and its applications in large scale data mining problems. Ph.D. thesis, University of Nebraska at Omaha (2006)Google Scholar
  5. 5.
    Kou, G., Liu, X., Peng, Y., Shi, Y., Wise, M., Xu, W.: Multiple criteria linear programming approach to data mining: models, algorithm designs and software development. Optim. Methods Softw. 18(4), 453–473 (2003)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Kovalerchuk, B., Vityaev, E.: Data Mining in Finance: Advances in Relational and Hybrid Methods, vol. 547. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  7. 7.
    Li, A., Shi, Y., He, J.: MCLP-based methods for improving bad catching rate in credit cardholder behavior analysis. Appl. Soft Comput. 8(3), 1259–1265 (2008)CrossRefGoogle Scholar
  8. 8.
    Liu, F., Shi, Y.: The search engine IQ test based on the internet IQ evaluation algorithm. Proc. Comput. Sci. 31, 1066–1073 (2014)CrossRefGoogle Scholar
  9. 9.
    Liu, F., Shi, Y., Liu, Y.: Intelligence quotient and intelligence grade of artificial intelligence. Ann. Data Sci. 4(2), 179–191 (2017)CrossRefGoogle Scholar
  10. 10.
    Liu, F., Shi, Y., Wang, B.: World search engine IQ test based on the internet IQ evaluation algorithms. Int. J. Inf. Technol. Decis. Making 14(02), 221–237 (2015)CrossRefGoogle Scholar
  11. 11.
    McGarry, K.: A survey of interestingness measures for knowledge discovery. Knowl. Eng. Rev. 20(1), 39–61 (2005)CrossRefGoogle Scholar
  12. 12.
    Nonaka, I., Toyama, R., Konno, N.: SECI, Ba and leadership: a unified model of dynamic knowledge creation. Long Range Plan. 33(1), 5–34 (2000)CrossRefGoogle Scholar
  13. 13.
    Peng, T., Zuo, W., He, F.: SVM based adaptive learning method for text classification from positive and unlabeled documents. Knowl. Inf. Syst. 16(3), 281–301 (2008)CrossRefGoogle Scholar
  14. 14.
    Peng, Y., Kou, G., Shi, Y., Chen, Z.: A descriptive framework for the field of data mining and knowledge discovery. Int. J. Inf. Technol. Decis. Making 7(04), 639–682 (2008)CrossRefGoogle Scholar
  15. 15.
    Romero, C., Ventura, S.: Data mining in education. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 3(1), 12–27 (2013)Google Scholar
  16. 16.
    Shi, Y.: Multiple Criteria and Multiple Constraint Levels Linear Programming: Concepts, Techniques and Applications. World Scientific Publishing Company, Singapore (2001)CrossRefGoogle Scholar
  17. 17.
    Shi, Y.: Big data: History, current status, and challenges going forward. Bridge 44(4), 6–11 (2014)Google Scholar
  18. 18.
    Shi, Y., Li, X.: Knowledge management platforms and intelligent knowledge beyond data mining. In: Shi, Y., Olsen, D.L., Stam, A. (eds.) Advances in Multiple Criteria Decision Making and Human Systems Management: Knowledge and Wisdom, pp. 272–288. IOS Press, Amsterdam (2007)Google Scholar
  19. 19.
    Shi, Y.: Introduction to Business Data Mining. New York, New York (2007)Google Scholar
  20. 20.
    Shi, Y., Peng, Y., Xu, W., Tang, X.: Data mining via multiple criteria linear programming: applications in credit card portfolio management. Int. J. Inf. Technol. Decis. Making 1(01), 131–151 (2002)CrossRefGoogle Scholar
  21. 21.
    Shi, Y., Tian, Y., Kou, G., Peng, Y., Li, J.: Optimization Based Data Mining: Theory and Applications. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-0-85729-504-0CrossRefzbMATHGoogle Scholar
  22. 22.
    Shi, Y., Wise, M., Luo, M., Lin, Y.: Data mining in credit card portfolio management: a multiple criteria decision making approach. In: Köksalan, M., Zionts, S. (eds.) Multiple Criteria Decision Making in the New Millennium. LNE, vol. 507, pp. 427–436. Springer, Heidelberg (2001).  https://doi.org/10.1007/978-3-642-56680-6_39 CrossRefzbMATHGoogle Scholar
  23. 23.
    Turner, V., Gantz, J.F., Reinsel, D., Minton, S.: The digital universe of opportunities: rich data and the increasing value of the internet of things. IDC Anal. Future 16 (2014)Google Scholar
  24. 24.
    Yan, N., Shi, Y., Chen, Z.: Multiple criteria nonlinear programming classification with the non-additive measure. In: Ehrgott, M., Naujoks, B., Stewart, T., Wallenius, J., et al. (eds.) Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems. LNE, vol. 634, pp. 289–297. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-04045-0_25CrossRefGoogle Scholar
  25. 25.
    Zhang, D., Tian, Y., Shi, Y.: A regression method by multiple criteria linear programming. In: 19th International Conference on Multiple Criteria Decision Making (2008)Google Scholar
  26. 26.
    Zhang, J., Shi, Y., Zhang, P.: Several multi-criteria programming methods for classification. Comput. Oper. Res. 36(3), 823–836 (2009)CrossRefGoogle Scholar
  27. 27.
    Zhang, L., Li, J., Shi, Y., Liu, X.: Foundations of intelligent knowledge management. Hum. Syst. Manag. 28(4), 145–161 (2009)CrossRefGoogle Scholar
  28. 28.
    Zhang, L., Li, J., Zheng, X., Li, X., Shi, Y.: Study on a process-oriented knowledge management model. JAIST PRESS Publications (2007). http://hdl.handle.net/10119/4140

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Economics and ManagementUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.Key Lab of Big Data Mining and Knowledge ManagementChinese Academy of SciencesBeijingChina
  3. 3.Research Center on Fictitious Economy and Data ScienceChinese Academy of SciencesBeijingChina
  4. 4.College of Information Science and TechnologyUniversity of Nebraska at OmahaOmahaUSA
  5. 5.School of Computer and ControlUniversity of Chinese Academy of SciencesBeijingChina

Personalised recommendations