Information system data mining based on improved DT algorithm
Weiwei Liu · Australian Journal of Electrical & Electronics Engineering · 2025
As information systems continue to expand in scale, thechallenge of mining and analyzing large volumes of data becomes increasingly complex. A significant issue arises from the perception that, due to limited information processing capabilities, effective analysis of vast datasets is not feasible. To address this, data extraction technology plays a crucial role in the development and application of modern information systems. This paper explores the foundational elements of data mining technology and its application strategies within information systems, and it introduces a novel data mining method based on an improved Decision Tree (DT) algorithm. The objective is to deepen societal understanding of data mining technology and enhance the effectiveness of information systems in serving the public. Experiments were conducted using UCI datasets, comparing proposed method to traditional DT methods across several metrics, including decision tree construction time, scale, classification accuracy, incremental mining accuracy, and load balancing. The experimental results demonstrate that the improved method significantlyoutperforms the traditional approach: decision tree construction time isreduced by 25%, the construction scale is decreased by 34%, and classification accuracy reaches 92%. These outcomes indicate the method’s superiority in handling large-scale data with greater efficiency, accuracy, and balance for modern information systems.