A Three-Stage Consensus-Based Method for Collective Knowledge Determination
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.
KeywordsCollective knowledge Consensus method Three-stage consensus-based
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).
- 2.Ramesh, C.D.: Handbook of Research on Economic, Financial, and Industrial Impacts on Infrastructure Development. IGI Global, USA (2017)Google Scholar
- 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
- 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.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.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
- 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.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
- 18.Wang, J., Su, X.: An improved K-means clustering algorithm. In: Proceedings of ICCSN 2011, pp. 44–46, IEEE (2011)Google Scholar