Association Rules on Attributes of Illicit Drugs, Suspect’s Demographics and Offence Categories

Abstract

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.