Model Mining Method for Collaborative Behavior of Knowledge Agent in Innovation Ecosystem
Conventional model of cooperative behavior mining method, can carry on the analysis, data mining to the conventional collaborative behavior but for specific subject knowledge in innovation ecosystem cooperative behavior, and analysis of the data mining results shooting low deficiencies, therefore puts forward innovation ecosystem in knowledge collaborative behavior main body model of the mining method. Based on knowledge innovation ecosystem in the main body composition analysis of collaborative behavior model, used algebraic representation, data processing design collaborative behavior model, realized the coordinated behavior model of innovation ecosystem knowledge subject data processing; According to the parameter fitting of collaborative behavior of knowledge subject in innovation ecosystem, the mining results were displayed to realize the model mining of collaborative behavior of knowledge subject in innovation ecosystem. The experimental data show that the proposed collaborative behavior model mining method is 41.84% higher than the traditional mining method, which is suitable for the model mining of collaborative behavior of knowledge subjects in the innovation ecosystem.
KeywordsInnovation ecosystems Knowledge subject Cooperative behavior Model mining
Supported by the National Natural Science Foundation of China (Grant No. 71771161).
Suzhou Science and Technology Program (Soft Science) Project (Grant No. SR201710).
- 1.Li, F., Chen, T.T.: Research on modeling and simulation of public network cooperative protection. Comput. Simul. 34(6), 298–301 (2017)Google Scholar
- 2.Zheng, S.Z., Wu, Q.: Research on urban innovation capability from the perspective of intellectual property rights and construction of innovation ecosystem——taking Zhuhai as an example. Sci. Technol. Manag. Res. 36(5), 111–116 (2017)Google Scholar
- 3.Tong, Z.H., Han, C.H.: Research on the impact of environment disturbance on NKCB and KCB in innovation activities. Sci. Technol. Prog. Policy 34(13), 136–143 (2017)Google Scholar
- 4.Min, X.P., Shi, Y.L., Li, H., et al.: Mining collaborative behavior based on dynamic supply chain network. Comput. Integr. Manuf. Syst. 22(2), 324–329 (2016)Google Scholar
- 5.Chen, J.B., Gao, S.L., Guo, Y.L.: Research on governance strategy of innovation ecosystem in small and medium-sized knowledge enterprises. Technoeconomics Manag. Res. 25(10), 26–30 (2017)Google Scholar
- 6.Li, X.M., Bao, F.N.: Research on the impact of university social capital and collaborative behavior on collaborative innovation performance. Sci. Technol. Prog. Policy 34(4), 122–128 (2017)Google Scholar
- 7.Fang, G., Zhou, Q., Yang, W.: Research context and progress from industry-university-institute cooperation to collaborative innovation: based on bibliometrics analysis. Technol. Econ. 35(10), 26–33 (2016)Google Scholar
- 8.Zhang, Y.Y., Zhang, S.T., Peng, H.J., et al.: Cyberspace knowledge innovation behavior under innovation ecosystem perspectives——multiple case studies based on grounded theory. Sci. Technol. Prog. Policy 34(6), 139–146 (2017)Google Scholar
- 9.Shan, M.M., You, J.X., Shao, J.: Co-evolution and optimization modes of industrial innovation ecosystem: a case study of Zhangjiang bio-pharmaceutical industry. Shanghai Manag. Sci. 39(3), 1–7 (2017)Google Scholar
- 10.Mao, B.Q., Deng, W., Feng, S., et al.: Calculation method of vertical target dispersion based on multidisciplinary collaboration simulation. Comput. Simul. 33(1), 20–23 (2016)Google Scholar