Discovery of Association Rules in Temporal Databases

Temporal databases naturally contain a wealth of information that can be unearthed by knowledge discovery and data mining techniques. Discovering association rules in market basket data have been widely studied and many algorithms have been developed. In this study, we examine discovery of association rules in temporal databases. We use the enumeration operation of the temporal relational algebra to prepare the data for discovery of association rules. To observe the changes in association rules and their statistics over the time, we can apply an incremental association rule mining technique to a series of datasets obtained over consecutive time intervals