Abstract
With the development of big data technology, creating the ‘Digital Campus’ is a hot issue. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, but the traditional clustering algorithm does not take the clustering efficiency and clustering effect into consideration. In this paper, the algorithm based on K-Means and clustering by fast search and find of density peaks (K-CFSFDP) is proposed, which improves on the distance and density of data points. This method is used to cluster students from four universities. The experiment shows that K-CFSFDP algorithm has better clustering results and running efficiency than the traditional K-Means clustering algorithm, and it performs well in large scale campus data. Additionally, the results of the cluster analysis show that the students of different categories in four universities had different performances in living habits and learning performance, so the university can learn about the students’ behavior of different categories and provide corresponding personalized services, which have certain practical significance.
Highlights
With the continuous development of information technology, the data accumulated in the campus information environment is gradually expanding, and a complete campus big data environment has been formed
The K-CFSFDP algorithm based on K-Means and CFSFDP was proposed to analyze different university students’ behaviors
We first introduced the relevant research on the behavior analysis of university students, and clarified that educational data mining was the current development trend
Summary
With the continuous development of information technology, the data accumulated in the campus information environment is gradually expanding, and a complete campus big data environment has been formed. C.Y. Yang analyzed the characteristics of big data in university campus and adopted K-Means algorithm to propose an early warning system of college students’ behavior based on the Internet of Things and big data environment [28]. We applied the relevant theories and knowledge of data mining and machine learning to the analysis of university students’ behavior. This is an application innovation in the field of machine learning and education. The K-Means and clustering by fast search and find of density peaks (K-CFSFDP) proposed in this study can automatically determine the number of student behavior types and typical representatives based on data. The running time is shorter and the running efficiency is higher, which has application advantages in the environment of campus big data
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