Mining irregular association rules based on action & non-action type data

Conventional positive association rules are the patterns that occur frequently together. These patterns represent what decisions are routinely made based on a set of facts. Irregular association rules are the patterns that represent what decisions are rarely made based on the same set of facts. Many domains like Healthcare, Banking etc need the irregular rule to improve their system. In this paper, we propose a level wise search algorithm that works based on action and non-action type data to find irregular association rules. We have observed that irregular association rules can be discovered efficiently based on action type and non-action type data from large database. To the best of our knowledge, there is no algorithm that can determine such type of associations. Its effectiveness has been demonstrated by testing it for a real world patient data set.