An approach to discovering temporal association rules

Abstract

The goal of discovering association rules is to discover all possible associations that accomplish certain restrictions (minimum support and confidence and interesting). However, it is possible to find interesting associations with a high confidence level but with little support. This problem is caused by the way support is calculated, as the denominator represents the total number of transactions in a time period when the involved items may have not existed. If, on the other hand, we limit the total transactions to the ones belonging to the items' lifetime, those associations would be now discovered, as they would count on enough support. Another difficulty is the large number of rules that could be generated, for which many solutions have been proposed. Using age as an obsolescence factor for rules helps reduce the number of rules to be presented to the user. In this paper we expand the notion of association rules incorporating time to the frequent itemsets discovered. The concept of temporal support is introduced and, as an example, the known algorithm A priori is modified to incorporate the temporal notions. © 2000 ACM.