Mining intelligent knowledge from a two-phase association rules mining

Association rule mining generates large quantities of rules, but not all of them are useful for decision making. In order to find the genuine useful knowledge for decision making, we propose an intelligent knowledge discovery model which is a new purpose-oriented approach based on a second order mining from association rules. More specifically, our model consists of two phases. In the first phase, proper objective measures are selected according to the user's goal. In the second phase, we define a new concept of rule utility measure as the subjective evaluation which incorporates user's goal, expert's experience, and domain knowledge. By doing so, the intelligent knowledge. which can support special strategies can be obtained. Experiments on two real world databases validate the effectiveness of our new model.