A temporal association rule is an association rule that holds during specific time intervals. An example is that eggs and coffee are frequently sold together in morning hours. This paper studies temporal association rules during the time intervals specified by user-given calendar schemas. Generally, the use of calendar schemas makes the discovered temporal association rules easier to understand. An example of calendar schema is (year, month, day), which yields a set of calendar-based patterns of the form 〈 d₃, d₂, d₁〉, where each d_i is either an integer or the symbol *. For example, 〈2000, *, 16〉 is such a pattern, which corresponds to the time intervals, each consisting of the 16th day of a month in year 2000. This paper defines two types of temporal association rules: precise-match association rules that require the association rule hold during every interval, and fuzzy-match ones that require the association rule hold during most of these intervals. The paper extends the well-known Apriori algorithm, and also develops two optimization techniques to take advantage of the special properties of the calendar-based patterns. The experiments show that the algorithms and optimization techniques are effective.