Asymmetric Objective Measures Applied to Filter Association Rules Networks

In this paper, the Filtered-Association Rules Network (Filtered-ARN) is presented to structure, prune, and analyze a set of association rules to construct candidate hypotheses. The Filtered-ARN algorithm selects association rules with the use of asymmetric objective measures, Added Value and Gain, then builds a network allowing more exploration information. The Filtered-ARN was validated using three datasets: Lenses and Soybean Large, both available online for a text and a real dataset with data on organic fertilization (Green Manure). The results were validated by comparing the Filtered-ARN with the conventional ARN and also comparing the results with the decision tree. The approach presented promising results, showing its ability to explain a set of objective items and the aid to build more consolidated hypotheses by guaranteeing statistical dependence with the use of objective measures.