Probability-based incremental association rule discovery algorithm

In dynamic databases, new transactions are appended as time advances. This may introduce new association rules and some existing association rules would become invalid. Thus, the maintenance of association roles for dynamic databases is an important problem. In this paper, probability-based incremental association rule discovery algorithm is proposed to cleat with dais problem The proposed algorithm uses the principle of Bernoulli trials to find expected frequent itemsets. This can reduce a number of times to scan CM original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of and updated database efficiently. The simulation results show dial the proposed algorithm has a good performance.