Research Of An Improved Apriori Algorithm In Student Psychological Analysis

With the rapid development of the Internet, our society has walked into an Information age. Researchers from different areas need to find out valuable information from data, and transferring data into valuable information and knowledge has become the urgent need in today’s society. Thus data mining technology has come into being under this background, and the association rules algorithm is the main research content.College students’ psychological problems have caused widespread social concern, the analysis and study of college students’ mental health has become a focus for scholars at home and abroad. Mental health can affect the college students’ normal study life directly, and many psychological problems are troubling college students, bringing about many negative influences on them. Therefore, analyzing the psychology of college students, and using data mining techniques in college students’ psychological analysis can have great practical significance for college students’ mental health education.The article first introduces some theoretical knowledge related to the association rule mining. Then, it illustrates the ideas and implementation procedure of the Apriori algorithm. According to the deficiencies in Apriori algorithm, some improvements will be put forward in the basis of the Apriori algorithm based on Boolean matrix. After being improved, the algorithm not only can effectively shortens the transaction set initialized to Boolean matrix, but also reduce the incidence of candidate itemsets to some extent. As a result, the time and space complexity is reduced.Finally, based on the collected data from the psychological evaluation and basic information of college students, the improved Apriori algorithm will be combined with the system of mental correlation analysis between college students to conclude some rules and special points, which can offer a new way for college students’ mental health education.