Data Analysis of University Educational Administration Information Based on Prefixspan Algorithm

With the rapid development of data mining technology, a large amount of educational administration information data has been produced. How to make full use of these information resources and dig out valuable information is a hot topic in the research of colleges and universities. Compared with positive sequential pattern mining, negative sequential pattern mining considers not only the events that have already occurred, but also the events that have not occurred, and it can assist decision making when simple positive sequential pattern mining may mislead decision making. And the existing sequential pattern mining algorithm has the same importance in each project when applied, which is impractical. In this paper, a data mining algorithm based on Prefixspan is proposed. During the mining process, different weights are set for the items, and the weighted support degree of each sequence is compared with the minimum support degree to obtain frequent sequence patterns. The algorithm is applied to the modeling and simulation of student data by using K-means clustering method. The experimental results show that this method can effectively improve the efficiency and accuracy of data mining, and has strong practicability.