Abstract
Generally the knowledge of a collective, which is considered as a representative of the knowledge states in a collective, is often determined based on a single-stage approach. For big data, however, a collective is often very large, a multi-stage approach can be used. In this paper we present an improvement of the two-stage consensus-based approach for determining the knowledge of a large collective. For this aim, clustering methods are used to classify a large collective into smaller ones. The first stage of consensus choice aims at determining the representatives of these smaller collectives. Then these representatives will be treated as the knowledge states of a new collective which will be the subject for the second stage of consensus choice. In addition, all the collectives will be checked for susceptibility to consensus in both stages of consensus choice process. Through experiments analysis, the improvement method is useful in minimizing the difference between single-stage and two-stage consensus choice approaches in determining the knowledge of a large collective.
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Vroom, V.H.: Leadership and the decision-making process. Organ. Dyn. 28(4), 82–94 (2000)
Nguyen, N.T.: Processing inconsistency of knowledge in determining knowledge of collective. Cybern. Syst. 40(8), 670–688 (2009)
Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. Springer, London (2008)
Day, W.H.E. The consensus methods as tools for data analysis. In: IFC 1987: Classifications and Related Methods of Data Analysis. North-Holland, Springer, Heidelberg (1988)
Kline, J.A.: Orientation and group consensus. Cent. States Speech J. 23(1), 44–47 (1972)
Saint, S., Lawson, J.R.: Rules for Reaching Consensus: A Modern Approach to Decision Making. Jossey-Bass, San Francisco (1994)
Herrera-Viedma, E., Herrera, F., Chiclana, F.: A consensus model for multiperson decision making with different preference structures. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 32(3), 394–402 (2002)
Eklund, P., Rusinowska, A., de Swart, H.: A consensus model of political decision-making. Ann. Oper. Res. 158(1), 5–20 (2008)
Wu, Z., Xu, J.: Consensus reaching models of linguistic preference relations based on distance functions. Soft. Comput. 16(4), 577–589 (2012)
Herrera-Viedma, E., et al.: A review of soft consensus models in a fuzzy environment. Inf. Fusion 17, 4–13 (2014)
Nguyen, N.T.: Using consensus methods for determining the representation of expert information in distributed systems. In: Cerri, S.A., Dochev, D. (eds.) AIMSA 2000. LNCS (LNAI), vol. 1904, pp. 11–20. Springer, Heidelberg (2000)
Nguyen, N.T.: Using consensus methods for solving conflicts of data in distributed systems. In: Jeffery, K., Hlaváč, V., Wiedermann, J. (eds.) SOFSEM 2000. LNCS, vol. 1963, pp. 411–419. Springer, Heidelberg (2000)
Nguyen, N.T.: Using consensus for solving conflict situations in fault-tolerant distributed systems. In: Proceedings of the First IEEE/ACM International Symposium on Cluster Computing and the Grid (2001)
Barthelemy, J.P., Guenoche, A., Hudry, O.: Median linear orders: heuristics and a branch and bound algorithm. Eur. J. Oper. Res. 42(3), 313–325 (1989)
Nguyen, T.N., Sobecki, J.: Using consensus methods to construct adaptive interfaces in multimodal web-based systems. Univ. Access Inf. Soc. 2(4), 342–358 (2003)
Maleszka, M., Nguyen, N.T.: Integration computing and collective intelligence. Expert Syst. Appl. 42(1), 332–340 (2015)
Du Nguyen, V., Nguyen, N.T.: A two-stage consensus-based approach for determining collective knowledge. In: Le Thi, H.A., Nguyen, N.T., Do, T.V. (eds.) Advanced Computational Methods for Knowledge Engineering. AISC, vol. 358, pp. 301–310. Springer, Heidelberg (2015)
Kozierkiewicz-Hetmańska, A.: Analysis of susceptibility to the consensus for a few representations of collective knowledge. Int. J. Softw. Eng. Knowl. Eng. 24(05), 759–775 (2014)
Kanungo, T., et al.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
Nguyen, N.T.: Methods for achieving susceptibility to consensus for conflict profiles. J. Intell. Fuzzy Syst. 17(3), 219–229 (2006)
Wang, H., Song, M.: Ckmeans.1d.dp: optimal k-means clustering in one dimension by dynamic programming. R J. 3(2), 29–33 (2011)
Nakamatsu, K., Abe, J.: The paraconsistent process order control method. Vietnam J. Comput. Sci. 1(1), 29–37 (2014)
Acknowledgement
We would like to thank Gideon Rosenblatt, Quoc Trung Bui, Erik Steiner for sharing datasets.
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Du Nguyen, V., Nguyen, N.T., Hwang, D. (2016). An Improvement of the Two-Stage Consensus-Based Approach for Determining the Knowledge of a Collective. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_10
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DOI: https://doi.org/10.1007/978-3-319-45243-2_10
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