Evaluating Association Rules and Decision Trees to Predict Multiple Target Attributes
Association rules and decision trees represent two well-known data mining techniques to find predictive rules. In this paper, the authors present a detailed comparison between constrained association rules and decision trees to predict multiple target attributes. They identify important differences between both techniques for such goal. They conduct an extensive experimental evaluation on a real medical data set to mine rules predicting disease on multiple heart arteries. The antecedent of association rules contains medical measurements and patient risk factors, whereas the consequent refers to the degree of disease on one artery or multiple arteries. Predictive rules found by constrained association rule mining are more abundant and have higher reliability than predictive rules induced by decision trees.