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Crime Linkage: a Fuzzy MCDM Approach

Auteur(s)
Albertetti, Fabrizio 
Institut du management de l'information 
Cotofrei, Paul 
Institut du management de l'information 
Grossrieder, Lionel
Ribaux, Olivier
Stoffel, Kilian 
Institut du management de l'information 
Maison d'édition
: IEEE
Date de parution
2013-6-4
De la page
1
A la page
3
Mots-clés
  • Crime analysis
  • crime linkage
  • fuzzy MCDM
  • Crime analysis

  • crime linkage

  • fuzzy MCDM

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.
Notes
, 2013
Nom de l'événement
IEEE Intelligence and Security Informatics (ISI) 2013
Lieu
Seattle
Lié au projet
An Intelligent Process-driven Knowledge Extraction Framework for Crime Analysis 
Identifiants
https://libra.unine.ch/handle/123456789/18355
Autre version
http://isiconference2013.org/
Type de publication
conference paper
Dossier(s) à télécharger
 main article: 2013-12-19_1135_6997.pdf (172.42 KB)
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