Jun Qi
ABSTRACT
This paper uses the method of association rules in data mining to analyze and research the psychological evaluation data of college students. Taking the psychological evaluation data of 2017 and 2018 college students in a university as the research object, the decision-making attributes of suspiciousness, depression, abnormal personality, and personal evaluation are respectively analyzed using Apriori algorithm. The association rules obtained from the experiment are further analyzed to find out the unfavorable factors affecting the mental health of college students.
- This work is supported by Qinghai University Youth Research Fund (2017-QGY-4)Google Scholar
- REFERENCESGoogle Scholar
- Ren Lijun. Design and Implementation of Data Mining System for Diagnosing Psychological Fault of College Students [J]. Journal of Dongguan University of Technology, 2008. 15(5): 55-60.Google Scholar
- Li Wengang. Design and implementation of mental health information service platform based on B/S architecture [D]. Hebei University of Science and Technology, 2012.Google Scholar
- Yao Guojing, Liu Guifen, Nong Leban, Thoughts on the construction of an open teaching system for mental health education in colleges and universities[J].Campus Psychology,2011.9(4): 261-262.Google Scholar
- Yang Bingru, Hu Jian, Song Wei. Typical applications on data mining technology in bioinformatics [J]. Computer Engineering and Applications. 2007,43(2):18-19.Google Scholar
- Eapen A. G. Application of data mining in medical applications[D].Citeseer,2004.Google Scholar
- Tan H., Dillon T. S., Hadzic F., IMB3-Miner: Mining Induced/Embedded subtrees by constraining the level of embedding, in Advances in Knowledge Discovery and Data Mining. 2006, Springer. p. 450-461. Google ScholarDigital Library
- Hadzic M., Hadzic F., Dillon T. Tree mining in mental health domain[C]. Hawaii International Conference on System Sciences, Proceedings of the 41st Annual: IEEE. 2008: 230-230. Google ScholarDigital Library
- Zahran, Sammy ;Peek, Lori; Snodgrass, Jeffrey G.; Weiler, Stephan; Hempel, Lynn. Economics of Disaster Risk, Social Vulnerability, and Mental Health Resilience, Risk Analysis,2011, v 31,n 7, p 1107-1119.Google Scholar
- Panova, Tayana; Lleras, Alejandro.Avoidance or boredom: Negative mental health outcomes associated with use of Information and Communication Technologies depend on users'motivations, Computers in Human Behavior, 2016,v 58, p 249-258. Google ScholarDigital Library
- Li, Jingqiang; Zhao, Ning; Hao, Bingxia .A new assessment model of mental health,Communications in Computer and Information Science,2015, v 529, p 328-333.Google Scholar
- Zhang Min, Yao Liangwei, Hou Yu. Research of frequent itemsets mining algorithm based on vector and matrix [J]. Computer Engineering and Design, 2013,03:939-943.Google Scholar
- Liu Yabo. Research and Application of Association Rules Mining Method[D]. Jilin: Jilin University, 2005.Google Scholar
- Liu Xuming, Kang Muning, Wang Xudu. Association Rules Mining Based on Matrix Algorithm [J]. Science Technology and Engineering, 2009,06:1604-16Google Scholar
Index Terms
- Analysis on the Influencing Factors of College Students' Mental Health Based on Data Mining
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