Application of Association Rule Mining in Talent Introduction Analysis

Zixuan Chen; Jiepin Ding; Zhiguang Zhou; Yin Zhu; Wenyu Zhang

doi:doi:10.11648/j.sjams.20190703.13

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Application of Association Rule Mining in Talent Introduction Analysis

Zixuan Chen, Jiepin Ding, Zhiguang Zhou, Yin Zhu, Wenyu Zhang

Published in Science Journal of Applied Mathematics and Statistics (Volume 7, Issue 3)

Received: 5 August 2019 Accepted: Published: 27 September 2019

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Abstract

With the advancement of higher education, many colleges have given increasing attention to talent introduction. On the other hand, the association rule mining technique is a useful method which extracts the useful association rules from the complex data repositories. This study takes the example of 245 academic staff from Zhejiang University of Finance and Economics, China and uses Apriori algorithm to explore the association rules on whether an academic staff can obtain the Natural Science Foundation of China (NSFC) within three years after s/he is recruited to the university. The aim of this study is to better introduce talents for colleges so that the academic levels of colleges can be improved. The results of association rule mining have shown that having published high quality papers such as SCI paper and SSCI paper has an important effect on the probability of academic staff to obtain NSFC within three years. Besides, the grade of PhD school has also an effect on the probability of academic staff to obtain NSFC within three years. The higher the grade of a staff’s PhD school is, the easier for him to obtain NSFC within three years.

Published in	Science Journal of Applied Mathematics and Statistics (Volume 7, Issue 3)
DOI	10.11648/j.sjams.20190703.13
Page(s)	45-50
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Data Mining, Association Rule Mining, Apriori Algorithm

References

[1]	Chien C. F., and Chen L. F. (2008). “Data mining to improve personnel selection and enhance human capital: a case study in high-technology industry.” Expert Systems with Applications, 34 (1): 280-290.
[2]	Wu X. D., Kumar V., and Quinlan J. R. et al. (2008). “Top 10 algorithms in data mining.” Knowledge and Information Systems, 14 (1): 1-37.
[3]	Shi Y. (2010). “A dimension reduction approach using shrinking for multi-dimensional data analysis.” International Journal of Intelligent Information Processing, 1 (2): 86-98.
[4]	Lin W., Alvarez S. A., and Ruiz C. (2002). “Efficient adaptive-support association rule mining for recommender systems.” Data Mining and Knowledge Discovery, 6 (1): 83-105.
[5]	Qodmanan H. R., Nasiri M., and Minaei-Bidgoli B. (2011). “Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence.” Expert Systems with Applications, 38 (1): 288-298.
[6]	Huang C., Lu R., and Choo K. K. R. (2016). “Secure and flexible cloud-assisted association rule mining over horizontally partitioned databases.” Journal of Computer and System Sciences, 89: 51-63.
[7]	Beiranvand V., Mobasher-Kashani M., and Bakar A. A. (2014). “Multi-objective PSO algorithm for mining numerical association rules without a priori discretization.” Expert Systems with Applications, 41 (9): 4259-4273.
[8]	Gyenesei A. (2001). “A fuzzy approach for mining quantitative association rules.” Acta Cybernetica, 15 (2): 305-320.
[9]	Fung K. Y., Kwong C. K., and Siu K. W. M. et al. (2012). “A multi-objective genetic algorithm approach to rule mining for affective product design.” Expert Systems with Applications, 39 (8): 7411-7419.
[10]	Alatas B., Akin E., and Karci A. (2008). “MODENAR: multi-objective differential evolution algorithm for mining numeric association rules.” Applied Soft Computing, 8 (1): 646-656.
[11]	Agrawal R., Imieliński T., and Swami A. (1993). “Mining association rules between sets of items in large databases.” In Proceedings of the ACM SIGMOD International Conference on Management of Data, May 26-28, Washington D. C., America, pp. 207-216.
[12]	Guo Y., Wang M., and Li X. (2017). “Application of an improved apriori algorithm in a mobile e-commerce recommendation system.” Industrial Management and Data Systems, 117 (2): 287-303.
[13]	Li Q., Zhang Y. Y., and Kang H. Y. et al. (2017). “Mining association rules between stroke risk factors based on the apriori algorithm.” Technology and Health Care, 25 (S1): 197-205.
[14]	Guo Z., Chi D., and Wu J. et al. (2014). “A new wind speed forecasting strategy based on the chaotic time series modelling technique and the apriori algorithm.” Energy Conversion and Management, 84: 140-151.
[15]	Singh J., Ram H., and Sodhi D. J. (2013). “Improving efficiency of apriori algorithm using transaction reduction.” International Journal of Scientific and Research Publications, 3 (1): 1-4.
[16]	Yu H., Wen J., and Wang H. et al. (2011). “An improved apriori algorithm based on the Boolean matrix and Hadoop.” Procedia Engineering, 15: 1827-1831.
[17]	Agrawal R., and Srikant R. (1994). “Fast algorithms for mining association rules.” In Proceedings of the 20th VLDB Conference, September 12-15, Santiago, Chile, pp. 487-499.

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APA Style

Zixuan Chen, Jiepin Ding, Zhiguang Zhou, Yin Zhu, Wenyu Zhang. (2019). Application of Association Rule Mining in Talent Introduction Analysis. Science Journal of Applied Mathematics and Statistics, 7(3), 45-50. https://doi.org/10.11648/j.sjams.20190703.13

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ACS Style

Zixuan Chen; Jiepin Ding; Zhiguang Zhou; Yin Zhu; Wenyu Zhang. Application of Association Rule Mining in Talent Introduction Analysis. Sci. J. Appl. Math. Stat. 2019, 7(3), 45-50. doi: 10.11648/j.sjams.20190703.13

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AMA Style

Zixuan Chen, Jiepin Ding, Zhiguang Zhou, Yin Zhu, Wenyu Zhang. Application of Association Rule Mining in Talent Introduction Analysis. Sci J Appl Math Stat. 2019;7(3):45-50. doi: 10.11648/j.sjams.20190703.13

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@article{10.11648/j.sjams.20190703.13,
  author = {Zixuan Chen and Jiepin Ding and Zhiguang Zhou and Yin Zhu and Wenyu Zhang},
  title = {Application of Association Rule Mining in Talent Introduction Analysis},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {7},
  number = {3},
  pages = {45-50},
  doi = {10.11648/j.sjams.20190703.13},
  url = {https://doi.org/10.11648/j.sjams.20190703.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20190703.13},
  abstract = {With the advancement of higher education, many colleges have given increasing attention to talent introduction. On the other hand, the association rule mining technique is a useful method which extracts the useful association rules from the complex data repositories. This study takes the example of 245 academic staff from Zhejiang University of Finance and Economics, China and uses Apriori algorithm to explore the association rules on whether an academic staff can obtain the Natural Science Foundation of China (NSFC) within three years after s/he is recruited to the university. The aim of this study is to better introduce talents for colleges so that the academic levels of colleges can be improved. The results of association rule mining have shown that having published high quality papers such as SCI paper and SSCI paper has an important effect on the probability of academic staff to obtain NSFC within three years. Besides, the grade of PhD school has also an effect on the probability of academic staff to obtain NSFC within three years. The higher the grade of a staff’s PhD school is, the easier for him to obtain NSFC within three years.},
 year = {2019}
}

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TY  - JOUR
T1  - Application of Association Rule Mining in Talent Introduction Analysis
AU  - Zixuan Chen
AU  - Jiepin Ding
AU  - Zhiguang Zhou
AU  - Yin Zhu
AU  - Wenyu Zhang
Y1  - 2019/09/27
PY  - 2019
N1  - https://doi.org/10.11648/j.sjams.20190703.13
DO  - 10.11648/j.sjams.20190703.13
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 45
EP  - 50
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20190703.13
AB  - With the advancement of higher education, many colleges have given increasing attention to talent introduction. On the other hand, the association rule mining technique is a useful method which extracts the useful association rules from the complex data repositories. This study takes the example of 245 academic staff from Zhejiang University of Finance and Economics, China and uses Apriori algorithm to explore the association rules on whether an academic staff can obtain the Natural Science Foundation of China (NSFC) within three years after s/he is recruited to the university. The aim of this study is to better introduce talents for colleges so that the academic levels of colleges can be improved. The results of association rule mining have shown that having published high quality papers such as SCI paper and SSCI paper has an important effect on the probability of academic staff to obtain NSFC within three years. Besides, the grade of PhD school has also an effect on the probability of academic staff to obtain NSFC within three years. The higher the grade of a staff’s PhD school is, the easier for him to obtain NSFC within three years.
VL  - 7
IS  - 3
ER  -

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Author Information

Zixuan Chen

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Jiepin Ding

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Zhiguang Zhou

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Yin Zhu

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Wenyu Zhang

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

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Plain Text BibTeX RIS

APA Style

Zixuan Chen, Jiepin Ding, Zhiguang Zhou, Yin Zhu, Wenyu Zhang. (2019). Application of Association Rule Mining in Talent Introduction Analysis. Science Journal of Applied Mathematics and Statistics, 7(3), 45-50. https://doi.org/10.11648/j.sjams.20190703.13

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ACS Style

Zixuan Chen; Jiepin Ding; Zhiguang Zhou; Yin Zhu; Wenyu Zhang. Application of Association Rule Mining in Talent Introduction Analysis. Sci. J. Appl. Math. Stat. 2019, 7(3), 45-50. doi: 10.11648/j.sjams.20190703.13

Copy | Download

AMA Style

Zixuan Chen, Jiepin Ding, Zhiguang Zhou, Yin Zhu, Wenyu Zhang. Application of Association Rule Mining in Talent Introduction Analysis. Sci J Appl Math Stat. 2019;7(3):45-50. doi: 10.11648/j.sjams.20190703.13

Copy | Download

@article{10.11648/j.sjams.20190703.13,
  author = {Zixuan Chen and Jiepin Ding and Zhiguang Zhou and Yin Zhu and Wenyu Zhang},
  title = {Application of Association Rule Mining in Talent Introduction Analysis},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {7},
  number = {3},
  pages = {45-50},
  doi = {10.11648/j.sjams.20190703.13},
  url = {https://doi.org/10.11648/j.sjams.20190703.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20190703.13},
  abstract = {With the advancement of higher education, many colleges have given increasing attention to talent introduction. On the other hand, the association rule mining technique is a useful method which extracts the useful association rules from the complex data repositories. This study takes the example of 245 academic staff from Zhejiang University of Finance and Economics, China and uses Apriori algorithm to explore the association rules on whether an academic staff can obtain the Natural Science Foundation of China (NSFC) within three years after s/he is recruited to the university. The aim of this study is to better introduce talents for colleges so that the academic levels of colleges can be improved. The results of association rule mining have shown that having published high quality papers such as SCI paper and SSCI paper has an important effect on the probability of academic staff to obtain NSFC within three years. Besides, the grade of PhD school has also an effect on the probability of academic staff to obtain NSFC within three years. The higher the grade of a staff’s PhD school is, the easier for him to obtain NSFC within three years.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Application of Association Rule Mining in Talent Introduction Analysis
AU  - Zixuan Chen
AU  - Jiepin Ding
AU  - Zhiguang Zhou
AU  - Yin Zhu
AU  - Wenyu Zhang
Y1  - 2019/09/27
PY  - 2019
N1  - https://doi.org/10.11648/j.sjams.20190703.13
DO  - 10.11648/j.sjams.20190703.13
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 45
EP  - 50
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20190703.13
AB  - With the advancement of higher education, many colleges have given increasing attention to talent introduction. On the other hand, the association rule mining technique is a useful method which extracts the useful association rules from the complex data repositories. This study takes the example of 245 academic staff from Zhejiang University of Finance and Economics, China and uses Apriori algorithm to explore the association rules on whether an academic staff can obtain the Natural Science Foundation of China (NSFC) within three years after s/he is recruited to the university. The aim of this study is to better introduce talents for colleges so that the academic levels of colleges can be improved. The results of association rule mining have shown that having published high quality papers such as SCI paper and SSCI paper has an important effect on the probability of academic staff to obtain NSFC within three years. Besides, the grade of PhD school has also an effect on the probability of academic staff to obtain NSFC within three years. The higher the grade of a staff’s PhD school is, the easier for him to obtain NSFC within three years.
VL  - 7
IS  - 3
ER  -

Copy | Download