The objective of data mining is to discover relationships among the data in a database. Temporal information can be used to provide a linear ordering on the occurrence of events, to determine inter-event relevance, and to link events in a data stream. The use of event linking extends the type of relationships that can be discovered. Standard market-basket analysis identifies co-occurrence in single transactions. Linking permits the discovery of relationships that occur among groups of events rather than strictly within a single event. Events may be linked by the source of the information, by relevancy constraints, and by duration. In this paper, we examine modifications to the a priori data mining algorithm suitable for identifying relationships in temporal data defined using event linking and fuzzy relevance constraints.