Mining Dynamic Databases using Probability-Based Incremental Association Rule Discovery Algorithm

In dynamic databases, new transactions are appended as time advances. This paper is concerned with applying an incremental association rule mining to extract interesting information from a dynamic database. An incremental association rule discovery can create an intelligent environment such that new information or knowledge such as changing customer preferences or new seasonal trends can be discovered in a dynamic environment. In this paper, probability-based incremental association rule discovery algorithm is proposed to deal with this problem. The proposed algorithm uses the principle of Bernoulli trials to find expected frequent itemsets. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has better performance than that of previous work.