Abstract

Because the data being mined in the temporal database will evolve with time, many researchers have focused on the incremental mining of frequent sequences in temporal database [3,8,9]. In this paper, we study the problem of discovering association rules that display regular cyclic variation over time. For example, bread and coffee are frequently sold together in morning hours, or moon cake, lantern and candle are often sold before Mid-autumn Festival. This paper extends the Apriori algorithm and develop the optimization technique for discovering cyclic association rules.