# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 5 Conclusions  
In this paper, 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. We noticed that clustering was an effective tool for behavior analysis of university students. We found that K-Means clustering algorithm had the disadvantage of not specifying K value and its clustering effect depended on the initial clustering center. Although CFSFDP clustering algorithm had good clustering results, its operation efficiency was low. Under the background of big data, the CFSFDP algorithm will be greatly restricted. Considering the clustering effect and running time, we constructed the K-CFSFDP algorithm as an effective tool to analyze the behavior of university students. In order to verify the effectiveness of the model, we collected data on the learning performance and living habits of 8000 students from four universities, and used K-Means, CFSFDP, and K-CFSFDP to cluster these data. We judged the clustering effect and operating efficiency through the value of SC, CHI, DBI, SSE, and the running time. Comparing and analyzing the experimental results, we could draw the following conclusions:  
1. University students with similar learning performance and living habits in each university gathered into a certain number of sets. 2. Clustering centers could reflect the behavioral characteristics of a certain category of students in the areas of learning performance and living habits. 3. The distribution of behavior categories of university students in different schools was not the same. 4. The K-CFSFDP algorithm could directly specify the appropriate k value and the optimal clustering center. That is, the algorithm could determine the number of student behavior types and behavior scores of each university. 5. K-CFSFDP had a better clustering effect than K-Means algorithm, and had a shorter running time than CFSFDP algorithm, so it could be applied to the analysis of university students' behavior. This study had certain practical significance in education and the pedagogical aspect. Teachers or school administrators could better obtain the categories and characteristics of student behavior. The practical value and significance of this study are as follows:  
First, this study could achieve a scientific and reasonable classification of university students' behavior and was simple to operate. For university decision makers and student administrators, they do not need to judge in advance which category a student belongs to. They only need to collect students' behavioral data, and then input them into the model for clustering and analysis. This facilitates the management process of decision makers and avoids preconceived judgments about student behavior types. Since it is based on the data clustering, the result is more objective and accurate. Second, this study can help school administrators provide targeted assistance to different student groups. The clustering results can show the differences between each type students, which can help schools better understand student behavior. Schools can analyze and summarize the behavior characteristics of different types of students, and take targeted measures for different types of students to help them have good living habits and perform well in learning. Third, the results of this research can provide feedback on the management effects of school administrators. For example, if a university has a small number of student groups, it means there exists a high similarity and concentrated distribution of students' behavior. This indicates that student life on campus may be tedious. As a result, school administrators should take measures to enrich students' campus life.