# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 5 Conclusions
If a university's student clusters are more distributed near certain values, or less distributed near certain values, education administrators can rethink what caused the imbalance in student distribution and take targeted improvement measures. For another example, if there are a large number of students in a certain group and a small number of students in another group, education administrators can think about what caused this difference, so as to provide corresponding assistance to the minority student groups. This can avoid ignoring the need of minority student groups. Fourth, the results of this research can help educators to further analyze what factors can affect and determine the behavior characteristics of students, such as using correlation analysis methods to study the relationship between students' personal characteristics (such as gender, height, weight, etc.) and student behavior. Fifth, this study provides a benchmark for the behavior classification of university students. Since K-CFSFDP determines the student behavior category, it is equivalent to providing a classification baseline about sample label. Therefore, we can use supervised machine learning methods to analyze university student behavior. This research was an application innovation of university student behavior analysis. There were application scope and applicable conditions. The application circumstances and data requirements of the algorithm proposed in this study were as follows:1. University student behavior data was structured data. Structured data refers to data that can be represented and stored in a relational database, and is represented in a two-dimensional form. The general characteristics are: the unit of data is \"row\", a row of data represents the information of an entity, and the attributes of each row of data are the same. In this study, each row of the data represented each student, and each column was the student's behavioral attributes (such as learning habits and living habits). 2. The behavior classification of college students was an unsupervised learning problem. Machine learning can be divided into supervised learning and unsupervised learning. There are data and labels in supervised learning, and machine learning can learn a function that maps data to labels. There are many forms of label definition, such as classifying students as \"good students\" or \"bad students\" based on the threshold of test scores. The data for unsupervised learning has no labels. In this study, we obtained student behavior data through statistics without any labels. That is, we did not define in advance which categories the students belong to, nor did we define the characteristics of each category, so it was an unsupervised learning problem. This means that from the distribution map of student behavior data (the abscissa is the learning score, the ordinate is the living score), we could not intuitively judge how many categories these data could be divided into, nor could we judge the typical representative data points of each category. We used K-CFSFDP to automatically classify students based on the similarity of data. 3. The scale of college students' behavior data was relatively large. First, the collection, calculation, and storage of big data are huge. Second, the dimensionality of the data is higher. Third, the data growth rate is very fast, and the data acquisition and processing speed is required to be fast. Fourth, the data value density is relatively low. In this study, the number of students in each university was often very large, reaching tens of thousands. This study counted data of a total of 8000 students, which had a certain number scale. In addition, we counted the data of each student's study and living habits. There were eight evaluation indicators (as shown in Tables 1 and 2), and the data had a certain dimension. The study in this paper had the following indications for further research. First, this paper only analyzed student behavior from two dimensions of living habits and learning performance.