Advertisement

A Three-Stage Consensus-Based Method for Collective Knowledge Determination

  • Dai Tho Dang
  • Van Du Nguyen
  • Ngoc Thanh Nguyen
  • Dosam HwangEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 769)

Abstract

Nowadays, the problem of referring knowledge from a large number of autonomous units for solving some problems in the real world has become more and more popular. The need for new techniques to process knowledge in collectives has become urgent because of the rapidly increasing in size of collectives. Many methods for determining the knowledge of collectives have been proposed; however, the traditional data processing methods are inadequate to deal with big collectives. In the present study, we propose a three-stage consensus-based method to determine the knowledge of a big collective. In particular, in the first stage, the sequence partitioning method is applied to partition a big collective into chunks having the same size. Then, the k-means algorithm is used for clustering each chunk into smaller clusters. The knowledge of each chunk is determined based on the knowledge of these clusters. Finally, the knowledge of the big collective is determined based on a set of the knowledge of the chunks. Simulation results have revealed the effectiveness of the proposed method in terms of the running time as well as the quality of the final collective knowledge of a big collective.

Keywords

Collective knowledge Consensus method Three-stage consensus-based 

Notes

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B4009410).

References

  1. 1.
    Subbu, K.P., Vasilakos, A.V.: Big data for context aware computing—perspectives and challenges. Big Data Res. 10, 33–43 (2017)CrossRefGoogle Scholar
  2. 2.
    Ramesh, C.D.: Handbook of Research on Economic, Financial, and Industrial Impacts on Infrastructure Development. IGI Global, USA (2017)Google Scholar
  3. 3.
    Nguyen, V.D., Nguyen, N.T.: A method for temporal knowledge integration using indeterminate valid time. J. Intell. Fuzzy Syst. 27(2), 667–677 (2014)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Maleszka, M., Nguyen, N.T.: Integration computing and collective intelligence. Expert Syst. Appl. 42(1), 332–340 (2015)CrossRefGoogle Scholar
  5. 5.
    Nguyen, N.T.: Methods for Consensus Choice and their Applications in Conflict Resolving in Distributed Systems, Wroclaw University of Technology Press (2002)Google Scholar
  6. 6.
    Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. Springer, London (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    Nguyen, V.D., Nguyen, N.T., Hwang, D.: An improvement of the two-stage consensus-based approach for determining the knowledge of a collective. Proc. of ICCCI 2016, 108–118 (2016)Google Scholar
  8. 8.
    Nguyen, V.D., Nguyen, N.T.: A two-stage consensus-based approach for determining collective knowledge. Proc. of ICCSAMA 2015, 301–310 (2015)Google Scholar
  9. 9.
    Kozierkiewicz, H-.A., Pietranik, M.: Assessing the quality of a consensus determined using a multi-level approach. In: Proceedings of INISTA 2017, pp. 131–136. IEEE (2017)Google Scholar
  10. 10.
    Ramakrishnan, R., Johannes, G.: Database Management Systems. McGraw-Hill, UK (2002)zbMATHGoogle Scholar
  11. 11.
    Nguyen, N.T.: Inconsistency of Collective of Knowledge and Collective Intelligence. Cybernet. Syst. Int. J. 39(6), 542–562 (2008)CrossRefzbMATHGoogle Scholar
  12. 12.
    Nguyen, N.T.: Processing inconsistency of knowledge in determining knowledge of collective. Cybernet. Syst. 40(8), 670–688 (2009)CrossRefzbMATHGoogle Scholar
  13. 13.
    Day, W.H.E.: The consensus methods as tools for data analysis. In: Bock, H.H. (eds.) Classification and Related Methods of Data Analysis, Proceedings of IFCS 1987, pp. 317–324, North-Holland (1987)Google Scholar
  14. 14.
    Gebala, M., Nguyen, V.D., Nguyen, N.T.: An analysis of influence of consistency degree on quality of collective knowledge using binary vector structure. In: Proceedings of ICCCI 2014, pp. 3–13. Springer (2015)Google Scholar
  15. 15.
    Nguyen, N.T.: Using Distance Functions to Solve Representation Choice Problems. Fundamenta Informat. 48(4), 295–314 (2001)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Oded, M., Lior, R.: Data Mining and Knowledge Discovery Handbook. Springer, US (2005)zbMATHGoogle Scholar
  17. 17.
    Mac Queen, J.E.: Some methods for classification and analysis of multivariate observations. Proc. Fifth Berkley Sympos. Math. 1(14), 281–297 (1967)MathSciNetGoogle Scholar
  18. 18.
    Wang, J., Su, X.: An improved K-means clustering algorithm. In: Proceedings of ICCSN 2011, pp. 44–46, IEEE (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Dai Tho Dang
    • 1
  • Van Du Nguyen
    • 2
  • Ngoc Thanh Nguyen
    • 2
  • Dosam Hwang
    • 1
    Email author
  1. 1.Department of Computer EngineeringYeungnam UniversityGyeongsanRepublic of Korea
  2. 2.Department of Information Systems, Faculty of Computer Science and ManagementWrocław University of Science and TechnologyWrocławPoland

Personalised recommendations