Abstract
With the rapid development of electronic computer and its software in recent decades, the information of basketball has become more and more obvious. It has become the goal of sports computing to use computer technology to help the athletes to train, to improve their competition level, and to assist the coaches make decision. Existing basketball technical and tactical analysis and statistical software owns several shortcomings such as poor motion control and real-time performance, small database capacity, weak ability of extracting video, low efficiency of game statistics algorithm. It predicts changes of the state inside the system by calculating the probability of state transition, which makes the prediction method own advantage in application and a high practical quality. This paper based on data mining technology, aiming at the characteristics of the analyzed object, selects the appropriate data mining methods and statistical analysis methods to solve the various problems encountered in the basketball match, which can obviously improve the sports level and management level.
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References
Huiqun, Z.H.A.O., Jing, S.U.N.: Sports computing: a new research domain of computer application. Comput. Sci. 31(8), 89–92 (2004)
Fang, Y.: Survey of data mining. Comput. Knowl. Technol, 17 (2007)
Wang, L.: The summarization of present situation of data mining research. Libr. Inf. 5, 41–46 (2008)
Zhang, C.: Current state of data mining research and its latest advance. J. Nanjing Inst. Ind. Technol. 3(2) (2003)
Costa, G., Ortale, R.: Model-based collaborative personalized recommendation on signed social rating networks. ACM Trans. Internet Technol. (TOIT) 16(3), 20 (2016)
Duwairi, R., Ammari, H.: An enhanced CBAR algorithm for improving recommendation systems accuracy. Simul. Model. Pract. Theory 60, 54–68 (2016)
Zhang, S., Wang, H., Huang, W.: Two-stage plant species recognition by local mean clustering and Weighted sparse representation classification. Clust. Comput. 20, 1–9 (2017)
Beel, J., Gipp, B., Langer, S., Breitinger, C.: paper recommender systems: a literature survey. Int. J. Digit. Libr. 17(4), 305–338 (2016)
Wang, Y., Li, J., Wang, H.H.: Cluster and cloud computing framework for scientific metrology in flow control. Clust. Comput., 1–10 (2017)
Acknowledgement
(1) National Social Science Foundation’s annual general project “Antitrust Economics Research on Vertical Restriction of Manufacturers” (15BJY002); (2) National Social Science Foundation’s later-stage funding project “Research on the Unbalance of Economic Development and the Adjustment and Upgrading of Industrial Structure” (17FJY014); (3) Research on the Evaluation System of the Quality of Higher Vocational Education in Hubei Province (2017ZDZB12), a major tendering project of the Educational Science Planning of Hubei Province in 2017; (4) Focus Research Base of Humanities and Social Sciences in Hubei Higher Education Institutions (Hubei Skilled Talents Training Research Center).
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Xiaohong, G., Yu, W. (2020). Analysis of Basketball Training Model Optimization Based on Artificial Intelligence and Computer Aided Model. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_257
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DOI: https://doi.org/10.1007/978-3-030-25128-4_257
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