Data mining applications have enormously altered the strategic decision-making processes of organizations. The application of association rules algorithms is one of the well-known data mining techniques that have been developed to cope with multidimensional databases. However, most of these algorithms focus on multidimensional data models for transactional data. As data warehouses can be presented using a multidimensional model, in this paper we provide another perspective to mine association rules in data warehouses by focusing on a measurement of summarized data. We propose four algorithms — VAvg, HAvg, WMAvg, and ModusFilter — to provide efficient data initialization for mining association rules in data warehouses by concentrating on the measurement of aggregate data. Then we apply those algorithms both on a non-repeatable predicate, which is known as mining normal association rules, using GenNLI, and a repeatable predicate using ComDims and GenHLI, which is known as mining hybrid association rules.