Context-Aware Rule Mining Using a Dynamic Transformer-Based Framework
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
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.
