IoT and Data Mining Techniques for Dynamic Digital Branding in E-commerce

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Abstract
With the rapid development of IoT technology, the e-commerce sector has encountered unprecedented opportunities and challenges. This study focuses on Xiaomi smart bands, collecting health and behavior data from 50 users over a three-month period. By integrating decision tree classification, K-means clustering, and association rule mining, we constructed a systematic brand-building analysis model. The results show that the decision tree classification model achieved an accuracy of 87.5%, effectively identifying user groups with high brand interaction frequency. K-means clustering further segmented these high-interaction users into three distinct groups, revealing significant differences in user behavior characteristics. Additionally, association rule mining uncovered key behavioral patterns, such as users with more than 8,000 steps per day being more likely to purchase sports shoes (support: 0.3, confidence: 0.85). These findings indicate that the proposed model can provide strong data support for brands in developing personalized marketing strategies and enhancing user experience. Although there are some limitations in data sources and model generalization, this study demonstrates the significant potential of combining IoT and data mining technologies in advancing e-commerce. Future research will aim to expand the dataset and optimize the model to enhance its applicability and practicality.