An enhanced incremental association rule discovery with a lower minimum support

In the real world of data, a new set of data has been being inserted into the existing database. Thus, the rule maintenance of association rule discovery in large databases is an important problem. Every time the new data set is appended to an original database, the old rule may probably be valid or invalid. This paper proposed the approach to calculate the lower minimum support for collecting the expected frequent itemsets. The concept idea is applying the normal approximation to the binomial theory. This proposed idea can reduce a process of calculating probability value for all itemsets that are unnecessary. In addition, the confidence interval is also applied to ensure that the collection of expected frequent itemsets is properly kept.