A Cluster Analysis Methodology for the Categorization of Soil Samples for Forensic Sciences Based on Elemental Fingerprint

• Published in: Applied artificial intelligence Vol. 36; no. 1
• Main Authors: Maione, Camila, Luíza da Costa, Nattane, Barbosa, Fernando, Barbosa, Rommel Melgaço
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Accuracy; Algorithms; Chemical composition; Chemical elements; Classification; Cluster analysis; Clustering; Crime; Data mining; Fingerprints; Footwear; Forensic science; Inductively coupled plasma mass spectrometry; Mass spectrometry; Scientists; Sediments; Soil chemistry; Support vector machines; Training; Vector quantization

Soil and dirt fragments are easily transferred from the ground to objects such as clothing, shoes, skin, nails, and tires. The elemental analysis of these samples involved in crimes can be an important source of information for forensic scientists because they can present substantial evidence by cre...