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Crime Linkage: a Fuzzy MCDM Approach
Auteur(s)
Maison d'édition
: IEEE
Date de parution
2013-6-4
De la page
1
A la page
3
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.
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
Identifiants
Autre version
http://isiconference2013.org/
Type de publication
conference paper
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