A Comparison of Association Rule Discovery and Bayesian Network Causal Inference Algorithms to Discover Relationships in Discrete Data

. Association rules discovered through attribute-oriented induction are commonly used in data mining tools to express relationships between variables. However, causal inference algorithms discover more concise relationships between variables, namely, relations of direct cause . These algorithms produce regressive structured equation models for continuous linear data and Bayes networks for discrete data. This work compares the effectiveness of causal inference algorithms with association rule induction for discovering patterns in discrete data. 1 Introduction Association rules discovered using attribute-oriented induction in tools such as DBMiner are used to express relationships among variables. However, causal inference algorithms discover deeper relationships, namely a variety of causal relationships including genuine causality, potential causality and spurious association [7,8]. In this paper, we describe and compare association rule generation based on their implementation..