Post-Processing Association Rules using Networks and Transductive Learning

Association is widely used to find relations among items in a given database. However, finding the interesting patterns is a challenging task due to the large number of rules that are generated. Traditionally, this task is done by post-processing approaches that explore and direct the user to the interesting rules of the domain. Some of these approaches use the user's knowledge to guide the exploration according to what is defined (thought) as interesting by the user. However, this definition is done before the process starts. Therefore, the user must know what may be and what may not be interesting to him/her. This work proposes a general association rule post-processing approach that extracts the user's knowledge during the post-processing phase. That way, the user does not need to have a prior knowledge in the database. For that, the proposed approach models the association rules in a network, uses its measures to suggest rules to be classified by the user and, then, propagates these classifications to the entire network using transductive learning algorithms. Therefore, this approach treats the post-processing problem as a classification task. Experiments were carried out to demonstrate that the proposed approach reduces the number of rules to be explored by the user and directs him/her to the potentially interesting rules of the domain.