Abstract
In this paper, a Normal University’s 2011–2016 real admissions data are analyzed by the Apriori, K-MEANS and KNN algorithm. The result shows that the university’s normal students are more likely to choose other normal majors than to choose other non-normal majors related the normal majors and the overall situation of the Normal University’s student enrollment is relatively stable. Liberal arts college is the most popular college. Chinese language and Literature (normal) and English (normal) are more popular in the Normal University. The result reveals the internal connection between the various majors and has a guiding role for specialties setup in the university.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li L, Liu Y. Analysis the factors influencing college entrance examinees filling the aspiration forms. China J Health Psychol. 2008.
Ma E, Kong X, Song G. Structure research on decision-making of filling the aspiration forms. China J Health Psychol. 2009.
Shen X, Sun S. Analysis of college entrance examination decision making based on statistical model. Stat Decis. 2014;21:57–9.
Xu G. Applying to college aided decision support system based on data mining. Comput Technol Autom. 2014;33(4):106–9.
Han XF, Liu XY. Design and implementation of a college entrance exam predict system based on web mining. Appl Res Comput. 2004;21(8):160–2.
Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases. In: International conference on very large data bases. Morgan Kaufmann Publishers Inc.;1994. p. 487–99.
Hartigan JA, Wong MA. A K-means clustering algorithm. Appl Stat. 1979;28(1):100–8.
Yuan F, Zhou Z, Song X. K-means clustering algorithm with meliorated initial center. Comput Eng. 2007;33(3):65–6.
Yang SL, Yong-Sen LI, Xiao-Xuan HU, et al. Optimization study on k value of K-means algorithm. Syst Eng Theory Pract. 2006;26(2):97–101.
Altman NS. An introduction to Kernel and nearest-neighbor nonparametric regression. Am Stat. 1992;46(3):175–85.
Acknowledgements
This work was supported by the open research foundation of the machine intelligence and advanced computing key laboratory of education ministry (MSC-201707A), overlapping research project of Capital Normal University; science and technology innovation platform project of Capital Normal University. The study is approved for the school of management, Capital Normal University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tan, Z., Wang, J., Peng, Y., Ma, F. (2018). The Admissions Big Data Mining Research Based on Real Data from a Normal University. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-6496-8_49
Download citation
DOI: https://doi.org/10.1007/978-981-10-6496-8_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6495-1
Online ISBN: 978-981-10-6496-8
eBook Packages: EngineeringEngineering (R0)