# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 1 Introduction  
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. Traditional campus management concepts and data analysis methods have been unable to meet the growing data processing needs. How to effectively manage and share campus data, use big data mining ideas to optimize student management, and provide clearer, more detailed data services for students is a problem faced by today's campus service system. At present, there is much research on university students' behavior, involving multiple aspects of behavior. Some researchers analyzed the college students' physical activities (PA) to find PA patterns and their determinants, which will help college students to notice their health conditions [1]. [PERSON] examined the prevalence of smoking, binge drinking, physical inactivity, and excessive bodyweight in a population of healthcare students attending an Italian university [2]. [PERSON] studied the university students' behavioral intention to use mobile learning [3]. [PERSON] investigated the influence of motivational factors and self-regulatory strategies on autonomous learning behavior [4]. Most of the above studies mainly used traditional mathematical statistical methods to obtain data information, but did not further explore the laws behind the data. Besides, it can be seen from these studies that the learning behavior and life behavior of university students are the main concerns of the school. Therefore, we mainly focus on the learning performance and living habits of university students, and classify them accordingly. Educational data mining (EDM) can discover hidden patterns and knowledge from a large amount of data. Many scholars have applied data mining to the analysis of university students' behaviors to find the laws behind their behaviors. For example, [PERSON] used data mining techniques to analyze learner preferences. The study found that field independent learners frequently use backward/forward buttons and spent less time for navigation, while field dependent learners often use main menu and have more repeated visiting. [5]. [PERSON] used the free comments written by students at the end of each class to continuously track students' learning and improve the prediction of students' final grades [6]. [PERSON] analyzed the relationship between risk behaviors and resilience among South Asian minority youth. They discussed the resilience-based research, practical, and social policy implications [7]. [PERSON] examined the association between prescription opioid misuse and suicidal ideation, suicide plan, and suicide attempt among adolescents. The research demonstrated the harmful effects of prescription opioid misuse and its association with suicidal behaviors among adolescents [8]. [PERSON] analyzed the course scores of 106 undergraduates and used the decision tree algorithm to discover the factors that affect the grades of the undergraduates' course [9]. [PERSON] applied decision tree algorithms on students' past performance data to generate the model that can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention [10]. Some of the above studies used supervised machine learning methods. For the classification method of supervised machine learning, the \"label\" of university students is determined, that is, the types of university students are defined in advance, and the work done is only to \"classify\" by analyzing the behavior data of university students. However, in most cases, the number of types of students in a university cannot be known in advance, so unsupervised machine learning methods are needed for clustering. Clustering algorithm is a very important technology in data mining. Clustering is the process of assigning samples to different classes.