Analysis of College Students’ Consumption Behavior Data Based on Fractional-Order Firefly Optimization Clustering Algorithm

Data mining-based student consumption behavior analysis is an important part of smart campus construction, which could find students’ eating patterns and consumption levels. Therefore, data mining-based student consumption behavior analysis became a hot topic both in research and industry areas. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, but the traditional clustering algorithm does not take the clustering efficiency and clustering effect into consideration. In this paper, the algorithm based on k-means and clustering by fractional-order firefly algorithm (FFA-k-means), which optimizes the clustering centers algorithm, is proposed. This method is used to cluster students from colleges. The experiment shows that the algorithm proposed in this paper has better clustering results compared with the traditional k-means clustering algorithm. Additionally, through the analysis results, it can be found that the problem of the group of students with too few times of consumption, the problem of a low number of students’ consumption of three meals, and the proportion of living diets is too low. The causes and characteristics of these problems are used as a reference for colleges to take corresponding measures timely.