Crime linkage: A fuzzy MCDM approach

Author

Albertetti, Fabrizio ; Cotofrei, Paul ; Grossrieder, Lionel ; Ribaux, Olivier ; Stoffel, Kilian

Author_Institution

Inf. Manage. Inst., Univ. of Neuchatel, Neuchatel, Switzerland

fYear

2013

fDate

4-7 June 2013

Firstpage

1

Lastpage

3

Abstract

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.

Keywords

criminal law; decision making; forensic science; fuzzy set theory; inference mechanisms; police data processing; behavioral information; burglary; computerized method; crime grouping; crime similarity; crimes property; criminal apprehension; forensic information; fuzzy MCDM approach; high-volume crime linkage; multicriteria decision making; reasoning; situational information; Cognition; Couplings; Databases; Decision making; Educational institutions; Forensics; Fuzzy sets; Crime analysis; crime linkage; fuzzy MCDM;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligence and Security Informatics (ISI), 2013 IEEE International Conference on

Conference_Location

Seattle, WA

Print_ISBN

978-1-4673-6214-6

Type

conf

DOI

10.1109/ISI.2013.6578772

Filename

6578772