mlsanityData to 14 Oct 2026

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

arXiv 2503.11125 ↗First posted 14 Mar 2025cs.LGcited by 7

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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.

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.

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13 of 30 references.

References · 13

  1. 2024Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Miningcited by 47
  2. 2022On Transforming Reinforcement Learning by Transformer: The Development Trajectorycited by 36
  3. 2024Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networkscited by 33
  4. 2025Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusioncited by 11
  5. 2025Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Datacited by 11
  6. 2025Object Detection for Medical Image Analysis: Insights from the RT-DETR Modelcited by 10
  7. 2024Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applicationscited by 9
  8. 2025Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentationcited by 9
  9. 2025A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Predictioncited by 9
  10. 2024AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAPcited by 7
  11. 2025Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformercited by 5
  12. 2025Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Modelscited by 4
1 more
  1. 2024Correlation analysis technique of key parameters for transformer material inspection based on FP-tree and knowledge graphcited by 1

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Cited by year

Citing papers per year: 2025: 7.

7 papers.

  1. 2025Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQNcited by 6
  2. 2025Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Servicescited by 6
  3. 2025Pre-trained Language Models and Few-shot Learning for Medical Entity Extractioncited by 4
  4. 2025Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networkscited by 4
  5. 2025Improving Harmful Text Detection with Joint Retrieval and External Knowledgecited by 2
  6. 2025Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modelingcited by 1
  7. 2025Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networkscited by 0