Advanced clustering methods for mining chemical databases in forensic science

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Frederic Ratle and Christian Gagne and 
                 Anne-Laure Terrettaz-Zufferey and Mikhail Kanevski and 
                 Pierre Esseiva and Olivier Ribaux",
  title =        "Advanced clustering methods for mining chemical
                 databases in forensic science",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  volume =       "90",
  number =       "2",
  pages =        "123--131",
  year =         "2008",
  ISSN =         "0169-7439",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1016/j.chemolab.2007.09.001",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Forensic
                 science, Machine learning, Pattern analysis, Spectral
                 clustering, Kernel methods, Gas chromatography",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:",
  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

Genetic Programming entries for Frederic Ratle Christian Gagne Anne-Laure Terrettaz-Zufferey Mikhail F Kanevski Pierre Esseiva Olivier Ribaux