Classification Model Based on Association Rules in Customs Risk Management Application
Wang Ya-qin, Yuming Song · 2010
At present, detecting customs declaration frauds with limited examination of imported goods by available scarce resources is posing considerable challenge to the customs authorities world over. Data mining techniques could be utilized to sift through the past data and develop predictive model for examination of limited goods with higher probability of fraud. This paper puts forward a classification data mining method based on association rules. Following the analysis on customs inspection results and the exploration on the regularity of “non-consistent between customs declaration and actual commodity” by use of data mining based on association rules, a classification model is established to predict the risk of commodity through customs clearance and form the reference for customs inspection and monitoring.