Discovery of association rules in medical data

Data mining is a technique for discovering useful information from large da tabases. This technique is currently being profitably used by a number of i ndustries. A common approach for information discovery is to identify assoc iation rules which reveal relationships among different items. In this pape r, we use this approach to analyse a large database containing medical-reco rd data. Our aim is to obtain association rules indicating relationships be tween procedures performed on a patient and the reported diagnoses. Random sampling was used to obtain these association rules. After reviewing the ba sic concepts associated with data mining, we discuss our approach for ident ifying association rules and report on the rules generated.