# Context-Aware Rule Mining Using a Dynamic Transformer-Based Framework

arXiv: https://arxiv.org/abs/2503.11125 · HTML: https://mlsanity.com/paper/2503.11125/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [On Transforming Reinforcement Learning by Transformer: The Development Trajectory](https://mlsanity.com/paper/2212.14164/) (2022)
- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion](https://mlsanity.com/paper/2502.03664/) (2025)
- [Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data](https://mlsanity.com/paper/2502.03803/) (2025)
- [Object Detection for Medical Image Analysis: Insights from the RT-DETR Model](https://mlsanity.com/paper/2501.16469/) (2025)
- [Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications](https://mlsanity.com/paper/2412.18222/) (2024)
- [Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation](https://mlsanity.com/paper/2502.03813/) (2025)
- [A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction](https://mlsanity.com/paper/2502.06847/) (2025)
- [AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP](https://mlsanity.com/paper/2501.14745/) (2024)
- [Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer](https://mlsanity.com/paper/2501.13467/) (2025)
- [Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models](https://mlsanity.com/paper/2501.14859/) (2025)
- [Correlation analysis technique of key parameters for transformer material inspection based on FP-tree and knowledge graph](https://mlsanity.com/paper/2403.05076/) (2024)

## Built on by (most cited first, 7 of 7)

- [Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQN](https://mlsanity.com/paper/2504.09344/) (2025)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Pre-trained Language Models and Few-shot Learning for Medical Entity Extraction](https://mlsanity.com/paper/2504.04385/) (2025)
- [Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2505.05989/) (2025)
- [Improving Harmful Text Detection with Joint Retrieval and External Knowledge](https://mlsanity.com/paper/2504.02310/) (2025)
- [Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modeling](https://mlsanity.com/paper/2508.10255/) (2025)
- [Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks](https://mlsanity.com/paper/2508.07122/) (2025)

## Abstract (arXiv)

This study proposes a dynamic rule data mining algorithm based on an improved Transformer architecture, aiming to improve the accuracy and efficiency of rule mining in a dynamic data environment. With the increase in data volume and complexity, traditional data mining methods are difficult to cope with dynamic data with strong temporal and variable characteristics, so new algorithms are needed to capture the temporal regularity in the data. By improving the Transformer architecture, and introducing a dynamic weight adjustment mechanism and a temporal dependency module, we enable the model to adapt to data changes and mine more accurate rules. Experimental results show that compared with traditional rule mining algorithms, the improved Transformer model has achieved significant improvements in rule mining accuracy, coverage, and stability. The contribution of each module in the algorithm performance is further verified by ablation experiments, proving the importance of temporal dependency and dynamic weight adjustment mechanisms in improving the model effect. In addition, although the improved model has certain challenges in computational efficiency, its advantages in accuracy and coverage enable it to perform well in processing complex dynamic data. Future research will focus on optimizing computational efficiency and combining more deep learning technologies to expand the application scope of the algorithm, especially in practical applications in the fields of finance, medical care, and intelligent recommendation.