Abstract:

Temporal association rules mining(TARM) is widely applied in many applications, it aims at mining rules within a certain interval of time. Most of the exiting algorithms for TARM need to scan several times of the database, or do not consider the different exhibition period of an individual item, so the efficiency of these algorithms are not enough. In this paper, we present a novel approach to investigating TARM, the proposed algorithm works in an incremental way which takes the different exhibition period of individual item into account, in order to reduce the cost of storage, and only the frequent 1item is stored, and efficient pruning techniques are adopted to reduce the scan times of the database, and it only needs at most one time to scan the whole data set to obtain all the temporal association rules. The  experimental results show that the proposed algorithm is efficient.

Key words: data mining;association rules;temporal mining