University Data Analysis Based On Weighted Positive And Negative Sequential Patterns

Compared with positive sequential pattern mining,the negative one,which can assist decision-making when simple positive sequential pattern mining may mislead decision,considers not only the events that have occurred,but also the events that have not.It is not pramatic to the fact that the existing sequential pattern mining algorithms have the same importance in the application of items.So it is solved by proposing weights.This paper proposes a data mining algorithm based on the weighted positive and negative sequential patterns.In the process of mining,different weights are set for the items,and frequent sequential pattern is obtained by the contrast between the weighted support of each sequence and the minimum support.It is using K-means clustering method for modeling simulation when the algorithm applies to the student's data.The experimental results show that the method improves the efficiency and accuracy of data mining effectively,and demonstrates the application of data mining technology in data analysis better,proving to be highly practical.With the rapid development of data mining technology,it has produced vast amount of data information of college.So there are many valuable information hidden in that complex information.What is a hot topic in college research recently is that how to make full of using the information resources of universities to mine out valuable information.When mining the data of college,people ahead us frequently used the decision tree,association rules and other mining methods,few of them used sequential pattern mining method.Under that situation,there is a fact that those studies have certain deficiencies in highlighting data priorities.Therefore,this paper applies the weighted positive and negative sequential patterns method to university data to make up for deficiencies mentioned above,and summarize the educating regular pattern of excellent student better through analyzing the students' school behavior,in order to guide the different types of students.