Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics
American Journal of Applied Mathematics
Volume 6, Issue 2, April 2018, Pages: 55-61
Received: Apr. 26, 2018;
Published: Apr. 27, 2018
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Wang Qin, School of Economics, Zhejiang University of Finance & Economics, Hangzhou, China
Zhang Kangkang, School of Economics, Zhejiang University of Finance & Economics, Hangzhou, China
Chen Huiting, School of Economics, Zhejiang University of Finance & Economics, Hangzhou, China
In recent years, the issues of the talents introduction have attracted more and more researchers' and college administrators' attention. In the era of big data, data mining technology is widely used in various fields and has achieved remarkable results. The application of data mining technology in the introduction of university talents is in the ascendant. This paper uses the effective information of 245 teachers recruited by Zhejiang University of Finance & Economics since 2011 to explore and model the association rules. It preprocesses the raw information data by hierarchical clustering, and use Apriori algorithm to obtain a set of rules for the paper score and the situation of receiving the National Foundation of China (NFC) in 3 years. These rules will provide a constructive guiding significance for the introduction of talents in Zhejiang University of Finance & Economics.
Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics, American Journal of Applied Mathematics.
Vol. 6, No. 2,
2018, pp. 55-61.
P. S. H. Leeflang, P. C. Verhoef, P. Dahlström, et al., “Challenges and solutions for marketing in a digital era,” Eur. Manag. J., 2014, vol. 32, pp. 1-12.
W. Altaf, M. Shahbaz and A. Guergachi, “Applications of association rule mining in health informatics: a survey,” Artif. Intell Rev, 2017, vol. 47, pp. 313-340.
R. Agrawal, T. Imielński, and A. Swami, “Mining association rules between sets of items in large databases,” In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D. C., ACM SIGMOD Record, May, 1993, pp. 207-216.
S. Chalmers, J. T. Fuller, T. A. Debenedictis, et al., “Asymmetry during preseason functional movement screen testing is associated with injury during a junior Australian football season,” J. Sci. Med. Sport., 2017, vol. 20, pp. 653–657.
D. Tripathi, B. Nigam, and D. R. Edla, “A novel web fraud detection technique using association rule mining,” Proc. Comput. Sci., 2017, vol. 115, pp. 274-281.
C. Y. Zhang, “Research on library personalized service based on apriori algorithm,” Agro. Food. Ind. Hi. Tec., 2017, vol. 28, pp. 2555–2559.
K. S. Lakshmi and G. Vadivu, “Extracting association rules from medical health records using multi-criteria decision analysis,” Procedia. Comput. Sci., 2017, vol. 115, pp. 290-295.
W. Zhang, S. Xu, and S. Zhang, “Association rule mining for reasonable curriculum arrangement: a case study of Zhejiang University of Finance and Economics,” Int. J. Inf. Proc. Manag., RoMEO, 2015, vol. 6, pp. 42-47.
C. Romero, M. I. López, J. M. Luna, et al., “Predicting students' final performance from participation in on-line discussion forums,” Comput. Educ., 2013, vol. 68, pp. 458-472.
R. Asif, A. Merceron, S. A. Ali, et al., “Analyzing undergraduate students' performance using educational data mining,” Comput. Educ., 2017, vol. 113, pp. 177-194.
K. Zhu, “Research based on data mining of an early warning technology for predicting engineering students' performance,” World. Trans. Eng. Technol. Educ., (WIETE), 2014, vol. 12, pp. 572-575.
W. Y. Zhang, H. L. He and S. Zhang, “Predicting the grades of students' graduation thesis using optimized gradient boost decision tree,” Int. J. Inf. Proc. Manag., 2017, vol. 8, pp. 9-16.
N. Sael, A. Marzak and H. Behja, “Multilevel clustering and association rule mining for learners' profiles analysis,” Int. J. Comput. Sci. Issues. (IJCSI), 2013, vol. 10, pp. 188-194.
P. Liu, L. Sun, J. Zhao, et al., “Study and application of data mining technologies on human resources management in colleges,”(in Chinese), Comput. Eng. Appl., 2008, vol. 44, pp. 201-204.
R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” In Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, September, 1994, pp. 487-499.