A dynamic composite approach for evaluating association rules

Association mining is one the many tasks in data mining. In this paper, we consider the problem of evaluating association rules, an integral post process in association mining. In the literature, different interestingness measures have been proposed to evaluate association rules. Given an association mining task, measures are selected according to a set of user-specified properties. However, in practice, due to the subjectivity and imperfection in property specifications, it is a non-trivial task to make appropriate measure selections. In our work, we propose a novel approach that dynamically evaluates association rules according to a composite and collective effect of multiple measures. In essence, our approach makes use of neural networks along with back-propagation learning capability to determine the relative importance of measures in evaluating association rules. The effectiveness of our approach is shown through a set of empirical simulations. To the best of our knowledge, this is the first time that neural networks are applied to evaluating association rules.