The aim of data mining is the discovery of patterns within data stored in databases. Mining for association rules is a data mining method that lends itself to formulating conditional statements such as "if customers buy product A then they also buy product B and C with a probability of 90 percent." We consider different extended concepts of basic association rules. One of these concepts, quantitative association rules, is discussed in detail. Quantitative association rules allow statements like "20 percent of customers who buy at least three units of product A also buy between five and ten units of B and two units of C." We show that the quantitative nature of data can be hidden from the algorithm that mines for association rules. Thus, any standard algorithm that solves the basic problem can be used to cope with quantitative association rules. The presentation of our approach includes the design of the database, algorithms and data structures, as well as experiments with a prototype implementation.