A temporal association rule is an association rule that holds during specific time intervals. Temporal databases contain rich information that can be extracted by knowledge discovery and data mining techniques. Some algorithms were proposed for mining temporal association rules. In the real world, temporal databases are continually appended or updated so the discovered rules need to be updated. Re-running the temporal mining algorithm every time is inefficient since it ignores the previously discovered rules, and repeats the work done previously. Also, existing incremental mining techniques cannot deal with temporal association rules. In this paper, an incremental algorithm to maintain the temporal association rules in the transaction database is proposed. The algorithm exploits the results of earlier mining to derive the final mining output. The results show a significant improvement over the traditional approach of mining the whole updated database. |
