## 4 Experiment Result and Analysis
### Analysis of Clustering Results  
Based on the improved k-means clustering algorithm outlined in this paper, a clustering analysis was performed on the overall consumption model, with the clustering centers displayed in Table 8.  
The analysis reveals distinct consumption patterns associated with each category of students:  
(1) Type 1: This group is relatively small and may represent an extreme consumption cohort. (2) Type 2: Characterized by a high average monthly balance, total consumption, and consumption frequency, this group engages in numerous campus activities. (3) Type 3: These individuals show a concentrated distribution with three notable characteristics: a low average monthly balance, minimal total consumption, and infrequent consumption patterns, indicating limited engagement in campus activities. (4) Type 4: This group is more dispersed, with consumption levels that fall within an intermediate range. Table 9 presents the results of the clustering centers for the living diet model derived from the improved k-means clustering algorithm. (1) Type 1 students tend to have lower living and other consumption levels. They usually prefer to dine outside of college, particularly for food. For such students, University administrators should prioritize food safety and personal health for vulnerable students. One recommended approach is for universities to collaborate with off-campus food regulatory authorities to create a \"whitelist for off-campus dining.\" This initiative would involve auditing the hygiene standards of nearby restaurants and disseminating safety guidelines for off-campus dining to students, thereby reducing food safety risks. (2) Type 2 students are characterized by the highest living consumption but low other consumption and minimal card surplus, indicating a lower overall consumption level. These students often eat on campus, and their consumption patterns are stable, suggesting that their families face economic challenges and practice frugality. For this situation, universities should provide extra support and resources for students from low-income backgrounds and pay closer attention to their living conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Deposit** & **Frequency** & **Monetary** \\ \hline
1 & 194.84 & 22.00 & 2331.90 \\
2 & 97.75 & 77.52 & 699.47 \\
3 & 56.08 & 24.91 & 137.48 \\
4 & 78.06 & 57.57 & 360.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Clustering results of DFM model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Life Consumption** & **Other Expense** & **Surplus** \\ \hline
1 & 99.21 & 8.51 & 10.30 \\
2 & 500.86 & 42.56 & 41.65 \\
3 & 622.72 & 227.71 & 189.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Clustering results of the living model. For instance, universities could establish \"dedicated service windows for economically disadvantaged students\" in campus dining facilities, offering low-cost, nutritious meal packages. Consumption data from campus cards can be utilized to automatically identify these students for non-symptomatic subsidies. Meanwhile, universities can collaborate with enterprises to donate daily necessities, which are distributed through a point-based redemption system to avoid the psychological pressure caused by direct financial assistance. (3) Type 3 students exhibit the highest other living expenses and have the largest card surpluses, along with elevated living and dining costs. This indicates that they belong to a high-consumption group. Universities should facilitate students' understanding of the detriments of irrational consumption and provide systematic guidance for rational consumption behaviors. Based on the improved k-means clustering algorithm in this dissertation, the students were classified into three categories after the average consumption patterns of morning, midday, and evening were clustered and analyzed.