Recent Research: Elicitation of Association Rules from Information on Customs Offences on the Basis of Frequent Motives

A basic customs mission is the fight against fraud and trafficking. The conditions for carrying out this task depend on the creation of economic problems as well as on the actions of the actors in charge of carrying it out. As part of the customs clearance process, in connexion with the growth of international trade, customs are now faced with a growing volume of goods. To restrict intrusive control, automated risk management is therefore necessary. In this paper , in order to recognise suspicious behaviour arising from customs regulation, we suggest an unsupervised classification method to derive information rules from a database of customs offences … The idea is to apply the Apriori principle in customs procedures, on the basis of regular reasons in the database relating to customs offences, in order to discover possible rules of association between customs offences.
For the purpose of extracting information governing the incidence of fraud, activity and an offence. This mass of sometimes heterogeneous and complex data thus creates new needs that must be able to be fulfilled by methods of information extraction. Inevitably, the determination of infringements includes the accurate recognition of threats. It is an original approach to constructing association rules in two steps focused on data mining or data mining. First, look for frequent patterns (support > = minimum support) and then create association rules from the frequent patterns (Trust > = Minimum Trust). Three key association rules were illustrated in the simulations carried out: the forecasting rules, the targeting rules and the neutral rules, with the inclusion of a third predictor of the importance of the law, the lift test. The first two rules have built trust in theAt least 50%. Control in the customs system, however, depends on both the administrative processes and the actions of men in the control process; we suggest, in future work, the creation of an unsupervised method of clustering adapted to the customs context, enabling the results to be interpreted at different levels of granularity in order to promote the understanding of the model.

Author(s) Details

Dr. Bi Bolou Zehero

Institut National Polytechnique – Houphouët Boigny, Yamoussoukro, Côte d’Ivoire and Direction Générale des Douanes, République Côte d’Ivoire F. Yu. Shariokv

Dr. Pacôme Brou
Ecole Supérieure Africaine des TIC- ESATIC, Abidjan-Treichville, Côte d’Ivoire.

Olivier Asseu
Institut National Polytechnique – Houphouët Boigny, Yamoussoukro, Côte d’Ivoire and Ecole Supérieure Africaine des TICESATIC, Abidjan-Treichville, Côte d’Ivoire.

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