# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 1 Introduction
In general, in the clustering results, samples in the same category tend to have greater similarities, while samples in different classes have greater dissimilarities. The goal of clustering is to divide the data based on a specific similarity measure. Clustering algorithm is suitable for the description of students' behaviors. Based on a cluster analysis of 320,000 students, [PERSON] found that there is a huge difference in the utilization rate of support services among different student groups [11]. [PERSON] performed cluster analysis on German nurse students quitting smoking [12]. [PERSON] investigated students' behavior by using cluster analysis as an unsupervised methodology in the field of education [13]. Some researchers have also conducted other student behavior cluster studies [14; 15; 16]. The above literature analysis shows that there are many papers that use data mining techniques to analyze student behavior analysis. However, there are not many papers on the analysis of college students' behavior using clustering methods. Some papers used the clustering algorithm, which did not take into account the clustering effect and efficiency to analyze the students' behaviors. Currently, commonly used clustering methods include: partition clustering [17], density clustering, hierarchy-based [18], model-based [19], and density-based [20]. K-Means clustering algorithm is a traditional clustering algorithm proposed by [PERSON], which is simple and efficient [21]. At the same time, it has the advantages of scalability and high efficiency for processing large data sets. The K-Means clustering algorithm has a wide range of applications [22; 23; 24; 25; 26]. Some scholars also apply K-Means to the behavior analysis of university students. For example, [PERSON] illustrated the use of K-Means clustering to analyze characteristics of learning behavior while learners engage in a problem-solving activity in an online learning environment [27]. [PERSON] 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]. [PERSON] proposed a new approach based on text mining techniques for predicting student performance using latent semantic analysis and K-means clustering methods [29]. Although the K-Means algorithm has many advantages, it still has fatal flaws. These defects will greatly limit the application of K-Means. It has two major drawbacks. (1) The value of k needs to be specified in advance. The number of manually assigned clusters is inaccurate. Each type of data has its own characteristics. Randomly specifying k for unknown data does not reflect the clustering effect of the data itself. So, the clustering result has a great deal of randomness. (2) The K-Means algorithm starts randomly assigning cluster centers. Different cluster centers often result in different final clustering results. The K-Means algorithm is heavily dependent on the initial cluster center, and the final iterated cluster center is not necessarily the global optimal cluster center. At the same time, there are many improvements to the K-Means algorithm, but these two problems cannot be solved effectively. There are some algorithms to optimize the value of k. The main approach to this point improvement is to assign a number of values to k by guessing in advance and determine the best value of k by measuring the sum of the squared clustering errors. This approach has made great progress compared to before, but it is inefficient. Under certain k values, the initial clustering may exist without solutions. Therefore, multiple clusters are required to obtain the clustering result. Although it is possible to obtain better k-values, it takes a lot of time to work in the early stage and is unrealistic for dealing with large amounts of data. The common algorithm in this class is K-Means++. There is also an optimization algorithm for clustering centers.