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.
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References
Subbu, K.P., Vasilakos, A.V.: Big data for context aware computing—perspectives and challenges. Big Data Res. 10, 33–43 (2017)
Ramesh, C.D.: Handbook of Research on Economic, Financial, and Industrial Impacts on Infrastructure Development. IGI Global, USA (2017)
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)
Maleszka, M., Nguyen, N.T.: Integration computing and collective intelligence. Expert Syst. Appl. 42(1), 332–340 (2015)
Nguyen, N.T.: Methods for Consensus Choice and their Applications in Conflict Resolving in Distributed Systems, Wroclaw University of Technology Press (2002)
Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. Springer, London (2008)
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)
Nguyen, V.D., Nguyen, N.T.: A two-stage consensus-based approach for determining collective knowledge. Proc. of ICCSAMA 2015, 301–310 (2015)
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)
Ramakrishnan, R., Johannes, G.: Database Management Systems. McGraw-Hill, UK (2002)
Nguyen, N.T.: Inconsistency of Collective of Knowledge and Collective Intelligence. Cybernet. Syst. Int. J. 39(6), 542–562 (2008)
Nguyen, N.T.: Processing inconsistency of knowledge in determining knowledge of collective. Cybernet. Syst. 40(8), 670–688 (2009)
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)
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)
Nguyen, N.T.: Using Distance Functions to Solve Representation Choice Problems. Fundamenta Informat. 48(4), 295–314 (2001)
Oded, M., Lior, R.: Data Mining and Knowledge Discovery Handbook. Springer, US (2005)
Mac Queen, J.E.: Some methods for classification and analysis of multivariate observations. Proc. Fifth Berkley Sympos. Math. 1(14), 281–297 (1967)
Wang, J., Su, X.: An improved K-means clustering algorithm. In: Proceedings of ICCSN 2011, pp. 44–46, IEEE (2011)
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).
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Dang, D.T., Du Nguyen, V., Nguyen, N.T., Hwang, D. (2018). A Three-Stage Consensus-Based Method for Collective Knowledge Determination. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_1
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DOI: https://doi.org/10.1007/978-3-319-76081-0_1
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