New application of association rules in teaching evaluation system
Lou Lanfang, Qingxian Pan, Xiuqin Qiu · 2010
Data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. In this paper we propose a novel association rules for data mining to improve the famous algorithm Apriori. The proposed approach uses the intersection operation to generate frequent item sets. It is different from the existing algorithm as it scans the database only one time and then uses the database to mine association rules. The proposed technique has been implemented in a teaching evaluation system, to enhance the foundation in performance evaluation for staff in teaching issues.