Application Of Association Rule Mining In College Enrollment And Admission

Data mining is such technology and methods used to acquire potential rules and to extract useful knowledge in mass data. Association rule analysis, firstly presented by R.Agrawal etc, is a common method in data mining. The project of mining association rules in transaction database is very important to data mining.Presently, data mining is mainly applied in book-borrowing and teaching affairs in college teaching managements, and some useful conclusions are got. Now, one senior middle school graduate could be admitted by universities, colleges and secondary professional schools, which would be resulted in many colleges facing a severe challenge in student enrollment and surviving between universities and secondary professional schools. But in many higher junior colleges, enrollment data analysis is conducted by traditional statistic methods to seize key factors influencing admitted students registration. This thesis is programmed to analysis influencing factors of enrollment and admission in higher junior colleges by association rule mining.Some students admitted by higher junior colleges are not registered to be a freshman of college, which maybe result from various factors such as student class, student registered resident and college-entrance scores, etc. Statistical analysis of enrollment and admission data is firstly conducted in this thesis, using available methods such as histogram,quantile plot,scatter diagram, and quantile-quantile plot, which contribute in two aspects:giving a way to study various influencing factors on enrolment rate and presenting reference to attribute generalization for association rule mining. And then, item sets of association rule are reasonablly selected for acquiring valid rules according to the above statistic analysis results.According to association rules obtained by the designed system presented in paper, the key factors of influencing actual enrollment rate of higher junior colleges can be achieved and then well understood. Such association rules, mined in the thesis, give reference to student admission and enrollment management, such as predicting actual number of entrance, making next planning and corresponding adjustment.