Description

To address the problem of weighted association rules mining is to assign weights to items. Association rules are a method for discovering interesting relations between variables in large databases. To propose a Self-Assigned weights technique for assign weights to items, instead of assigning weights by users.. Adverse Drug Events (ADE) is injuries due to medic action management rather than the underlying condition of the patient. The objective of our process is to automatically detect cases of ADEs by means of Data Mining, which are a set of statistical methods particularly suitable for the discovery of rules in large datasets Here, we using supervised rule induction methods (association rules) are used to discover ADE detection rules. The rules are then filtered, validated, and reorganized by the ADE Detection Records. The rules are described in a rule repository, and several statistics are automatically computed in every medical department, such as support & confidence value. In this project, we proposed a self-assigned weights using HITS method to discover positive and negative association rules, instead of assigning the weights by users. To avoid mining misleading and uninteresting rules, a new type parameter, called sawinterest, is proposed to eliminate the redundant rules.