Performance Study on Rule-based Classification Techniques across Multiple Database Relations

Classification is an important task in data mining and machine learning which has been studied extensively and has a wide range of applications. There are many classification problem occurs and need to be solved. There are different types of classification algorithms like tree-based, rule-based etc, are widely used. In this paper, a performance comparison of different rule-based classifiers across multiple database relations is presented. Empirical study on both real world and synthetic databases shows their efficiency and accuracy. General Terms Classification, Rule-based classifier, Decision Tree.