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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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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