Mining Calendar-Based Asynchronous Periodical Association Rules with Fuzzy Calendar Constraints

We propose a new representation of calendars such that users can specify fuzzy calendar constraints to discover asynchronous periodical association rules embedded in temporal databases. We borrow the fuzzy set theory and use the conjunction operation to construct fuzzy calendar patterns and each fuzzy calendar pattern represents an asynchronous periodical behavior. Moreover, different time intervals have different weights corresponding to their matching degrees to the specified fuzzy calendar pattern. An efficient algorithm is also proposed to find association rules with the specified fuzzy calendar pattern. Unlike level wise a priori-based approaches, our method scans the underlying database at most twice. In the first scan, frequent 2-itemsets with their weighted counts in the specified fuzzy calendar pattern are obtained and then all candidate item sets are generated from the discovered frequent 2-itemsets. Finally, all frequent item sets with their weighted counts in the specified fuzzy calendar pattern are discovered in one shot. Asynchronous periodical association rules in the specified fuzzy calendar pattern are then obtained