RT Article
T1 Association Rules on Attributes of Illicit Drugs, Suspect’s Demographics and Offence Categories
JF Journal of drug issues
VO 53
IS 4
SP 637
OP 646
A1 Atsa’am, Donald Douglas
A1 Gbaden, Terlumun
A1 Wario, Ruth Diko
A2 Gbaden, Terlumun
A2 Wario, Ruth Diko
LA English
YR 2023
UL https://krimdok.uni-tuebingen.de/Record/185919334X
AB Association rules mining technique was employed to extract 6 rules that show the co-occurrences of the attributes on illicit drug types, suspects’ demographics, and categories of drug offences. A dataset on 262 arrestees of various drug offences was utilized for rules extraction using the apriori algorithm. The rules reveal the different levels of involvement with various illicit drugs by suspects of varying ages. The established rules provide a form of drug suspects segmentation which could guide how drug control and intervention programs are designed and deployed. Further, the rules could serve as a reference tool for security agents when dealing with drug suspects and offenders.
K1 suspect’s age
K1 suspect’s gender
K1 non-drug peddler
K1 drug peddler
K1 association rule
DO 10.1177/00220426221140010