An efficient Apriori_Goal algorithm is proposed for constructing association rules in a relational database with predefined classification. The target parameter of the database specifies a finite number of goals \(Goal_k\) , for each of which the algorithm constructs association rules of the form \(X \Rightarrow Goal_k\) . The quality of the generated rules is characterized by five criteria: two represent rule frequency, two reflect rule reliability, and the fifth is a weighted sum of these four criteria. The algorithm enables the construction of both high-frequency and rare rules with low occurrence frequency but high reliability. The efficiency of the algorithm is based on two factors: the method of encoding the database and its partitioning into subsets linked to the target parameter. Time complexity estimates for rule construction are provided using a medical database as an example. The article proves several statements that justify the rule construction process.