Student Behavior Data Analysis Based on Association Rule Mining

Abstract With the advancement of intelligent campus data acquisition technology, student behavioral are growing in size, variety, and real-time throughput, posing challenges to storage capacity computing power traditional analysis methods. The study focuses on application association rule mining analysis. Data collection, storage, computation, all comprise integral parts a four-layer architecture, three-step process from “data preprocessing” “finding rules” “acquiring relevant knowledge” is described. existing algorithm updated address issues overscanning original dataset excess iterations. findings case reveal that number iterations modified greatly lessened, effectively improving efficiency massive dataset.