Data mining for association rules aims to discover interesting relationships among sets of items in a database. Very often these databases include some kind of temporal information, the most common being a temporal label indicating transaction date. Within the field of association rule mining temporal information has been used to obtain sequential association rules, periodic or cyclic association rules, calendric association rules, or event-driven association rules. The temporal component is also relevant when analyzing how association rules evolve if datasets are evaluated on different time-slices. On the other hand, in traditional association rules item attributes were usually Boolean, but many attributes in current databases are quantitative in nature. Fuzzy temporal association rules arise from the use of fuzzy sets to describe quantitative temporal and/or not temporal attributes of items in a database, and/or to introduce fuzzy temporal specifications for the rules a user is interested in; the use of fuzzy sets allows a linguistic interpretation of the rules and also provides means to handle the uncertainty present in attribute measurements. Depending on the rule pattern the final user is interested in, different methods for fuzzy temporal association rule mining can be found in the literature, with mining algorithms adapted to the rule model being used. WIREs Data Mining Knowl Discov 2014, 4:64–70. doi: 10.1002/widm.1116

Conflict of interest: The authors have declared no conflicts of interest for this article.

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