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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Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/299
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