  Recently, the cyclic association rules have been introduced in order to
discover rules from items characterized by their regular variation over time.
In real life situations, temporal databases are often appended or updated.
Rescanning the whole database every time is highly expensive while existing
incremental mining techniques can efficiently solve such a problem. In this
paper, we propose an incremental algorithm for cyclic association rules
maintenance. The carried out experiments of our proposal stress on its
efficiency and performance.
