An efficient algorithm to discover calendar-based temporal association rules

This work is to discover all calendar-based temporal association rules that may occur over any time interval in a temporal database. A user-given calendar schema, e.g., year, month, and day, is firstly adopted to specify the interesting time intervals as calendar patterns. Then, in every time interval, the frequent 2-itemsets are discovered along with their 1-star calendar patterns. After that, information of the rest k-star calendar patterns of the frequent 2-itemsets are levelwisely aggregated from their 1-star calendar patterns. A minimal set of candidate calendar patterns are generated and counted in the first scan of database. To avoid multiple scans over the database, all candidate itemsets are generated from frequent 2-itemsets and the Apriori downward property is utilized to reduce the number of candidate calendar patterns. Finally, all frequent itemsets with their frequent calendar patterns are discovered in one shot. Calendar-based temporal association rules are then obtained. Experimental results have shown that our method is more efficient than others.