In this paper, we extend the scope of mining association rules from traditional single-dimensional intratransaction associations, to multidimensional intertransaction associations. Intratransaction associations are the associations among items with the same transaction, where the notion of the transaction could be the items bought by the same customer, the events happened on the same day, and so on. However, an intertransaction association describes the association relationships among different transactions, such as “if(company) A's stock goes up on day 1, B's stock will go down on day 2, but go up on day 4.��? In this case, whether we treat company or day as the unit of transaction, the associated items belong to different transactions. Moreover, such an intertransaction association can be extended to associate multiple contextual properties in the same rule, so that multidimensional intertransaction associations can be defined and discovered. A two-dimensional intertransaction association rule example is “After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away,��? which involves two dimensions: time and space. Mining intertransaction associations poses more challenges on efficient processing than mining intratransaction associations. Interestingly, intratransaction association can be treated as a special case of intertransaction association from both a conceptual and algorithmic point of view. In this study, we introduce the notion of multidimensional intertransaction association rules, study their measurements—support and confidence—and develop algorithms for mining intertransaction associations by extension of Apriori. We overview our experience using the algorithms on both real-life and synthetic data sets. Further extensions of multidimensional intertransaction association rules and potential applications are also discussed.
- DB-DM: DATA MINING