Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

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Ressource 1Télécharger: BIB_6CF03968660D.P001.pdf (13143.99 [Ko])
Etat: Public
Version: Après imprimatur
ID Serval
serval:BIB_6CF03968660D
Type
Thèse: thèse de doctorat.
Collection
Publications
Titre
Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants
Auteur(s)
Terrettaz-Zufferey Anne-Laure
Directeur(s)
Ribaux Olivier
Codirecteur(s)
Esseiva Pierre
Institution
Université de Lausanne, Faculté de droit et des sciences criminelles
Adresse
Lausanne
Statut éditorial
Acceptée
Date de publication
02/2009
Langue
français
Nombre de pages
172 p.
Notes
REROID:R005049853 ill.
Résumé
Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.
Création de la notice
11/09/2009 14:49
Dernière modification de la notice
20/08/2019 15:26
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