Cluster Analysis of Students' Consumption Behavior Based on K-means++ Algorithm

The clustering effect quality of the classical Kmeans algorithm is very sensitive to the initialization of the clustering center. When different initial clustering centers are selected for clustering modeling, the obtained clustering effect is quite different. In order to obtain more stable clustering results, this paper uses the improved distributed K-means + + algorithm based on the K-means algorithm to cluster the consumption behavior of students' behavior data. The students are clustered into three categories, and the characteristics of the three groups of the clustering results are compared and analyzed. The experimental results show that it is feasible to use clustering eigenvalues such as consumption intensity and consumption frequency to assist school funding departments to carry out targeted poverty alleviation work. At the same time, it provides a solution for finding “hidden poor students” and finding “fake poor students”.