employing data mining to explore association rules in drug addicts

drug addiction is a major social, economic, and hygienic challenge that impacts on all the community and needs serious threat. available treatments are successful only in short-term unless underlying reasons making individuals prone to the phenomenon are not investigated. nowadays, there are some treatment centers which have comprehensive information about addicted people. therefore, given the huge data sources, data mining can be used to explore knowledge implicit in them, their results can be employed as a knowledge base of decision support systems to make decisions regarding addiction prevention and treatment. we studied participants of such clinics including 471 participants, where 86.2% were male and 13.8% were female. the study aimed to extract rules from the collected data by using association models. results can be used by rehab clinics to give more knowledge regarding relationships between various parameters and help them for better and more effective treatments. e.g. according to the findings of the study, there is a relationship between individual characteristics and lsd abuse, individual characteristics, the kind of narcotics taken, and committing crimes, family history of drug addiction and family member drug addiction.