Dynamic Weights Based Risk Rule Generation Algorithm for Incremental Data of Customs Declarations

Abstract

Aimed at shortcomings, such as fewer risk rules for assisting decision-making in customs entry inspection scenarios and relying on expert experience generation, a dynamic weight assignment method based on the attributes of customs declaration data and an improved dynamic-weight Can-Tree incremental mining algorithm are proposed. In this paper, we first discretize the customs declaration data, and then form composite attributes by combining and expanding the attributes, which is conducive to generating rules with risk judgment significance. Then, weights are determined according to the characteristics and freshness of the customs declaration data, and the weighting method is applied to the Can-Tree algorithm for incremental association rule mining to automatically and efficiently generate risk rules. By comparing FP-Growth and traditional Can-Tree algorithms experimentally, the improved dynamic-weight Can-Tree incremental mining algorithm occupies less memory space and is more time efficient. The introduction of dynamic weights can visually distinguish the importance level of customs declaration data and mine more representative rules. The dynamic weights combine confidence and elevation to further improve the accuracy and positive correlation of the generated rules.

Keywords

• data mining
• incremental data
• association rules
• weighted mining
• canonical order tree