Advanced clustering methods for mining chemical databases in forensic science

Details

Serval ID
serval:BIB_718ADECB9D81
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Advanced clustering methods for mining chemical databases in forensic science
Title of the conference
Chemometrics and Intelligent Laboratory Systems
Author(s)
Ratle Frédéric, Gagné Christian, Terrettaz-Zufferey Anne-Laure, Kanevski Mikhail, Esseiva Pierre, Ribaux Olivier
Publisher
Elsevier BV
Address
Bruges
ISSN
0169-7439
Publication state
Published
Issued date
02/2008
Peer-reviewed
Oui
Volume
90
Number
2
Pages
123-131
Language
english
Notes
Old month value: avril
Abstract
Heroin and cocaine gas chromatography data are analyzed using several clustering techniques. A database with clusters confirmed by police investigation is used to assess the potential of the analysis of the chemical signature of these drugs in the investigation process. Results are compared to standard methods in the field of chemical drug profiling and show that conventional approaches miss the inherent structure in the data, which is highlighted by methods such as spectral clustering and its variants. Also, an approach based on genetic programming is presented in order to tune the affinity matrix of the spectral clustering algorithm. Results indicate that all algorithms show a quite different behavior on the two datasets, but in both cases, the data exhibits a level of clustering, since there is at least one type of clustering algorithm that performs significantly better than chance. This confirms the relevancy of using chemical drugs databases in the process of understanding the illicit drugs market, as information regarding drug trafficking networks can likely be extracted from the chemical composition of drugs.
Keywords
Forensic science, Machine learning, Pattern analysis, Spectral clustering, Kernel methods, Gas chromatography
Web of science
Create date
22/11/2008 12:00
Last modification date
25/07/2020 6:19
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