Titre
Crime linkage: A fuzzy MCDM approach
Type
article de conférence/colloque
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Auteur(s)
Albertetti, F.
Auteure/Auteur
Grossrieder, L.
Auteure/Auteur
Ribaux, O.
Auteure/Auteur
Stoffel, K.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
Maison d’édition
IEEE
Titre du livre ou conférence/colloque
2013 IEEE International Conference on Intelligence and Security Informatics
Statut éditorial
Publié
Date de publication
2013
Résumé
Grouping crimes having similarities has always been interesting for analysts. Actually, when a set of crimes share common properties, the capability to conduct reasoning and the automation with this set drastically increase. Conjunction, interpretation and explanation based on similarities can be key success factors to apprehend criminals. In this paper, we present a computerized method for high-volume crime linkage, based on a fuzzy MCDM approach in order to combine situational, behavioral, and forensic information. Experiments are conducted with series in burglaries from real data and compared to expert results.
PID Serval
serval:BIB_518251A0BE3C
Date de création
2016-04-15T16:15:57.911Z
Date de création dans IRIS
2025-05-20T20:47:30Z