Filtered-ARN: Asymmetric objective measures applied to filter Association Rules Networks

Abstract

In this paper, the Filtered-Association Rules Network (Filtered-ARN) is presented to structure, prune, and analyze a set of association rules in order 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, Hayes-roth, and Soybean Large, available online. We carried out a concept proof experiment using a real dataset with data on organic fertilization (Green Manure) for text the proposed method. 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.