Abstract: Association rules are an important problem in data
mining. Massively increasing volume of data in real life databases
has motivated researchers to design novel and incremental algorithms
for association rules mining. In this paper, we propose an incremental
association rules mining algorithm that integrates shocking
interestingness criterion during the process of building the model. A
new interesting measure called shocking measure is introduced. One
of the main features of the proposed approach is to capture the user
background knowledge, which is monotonically augmented. The
incremental model that reflects the changing data and the user beliefs
is attractive in order to make the over all KDD process more
effective and efficient. We implemented the proposed approach and
experiment it with some public datasets and found the results quite
promising.