Pruning based interestingness of mined classification patterns

Abstract EN

Classification is an important problem in data mining.

Decision tree induction is one of the most common techniques that are applied to solve the classification problem.

Many decision tree induction algorithms have been proposed based on different attribute selection and pruning strategies.

Although the patterns induced by decision trees are easy to interpret and comprehend compare to the patterns induced by other classification algorithms, the constructed decision trees may contain hundreds or thousands of nodes which are difficult to comprehend and interpret by the user who examines the patterns.

For this reasons, the question of an appropriate constructing and providing a good pruning criteria have long been a topic of considerable debate.

The main objective of such criteria is to create a tree such that classification accuracy, when used on unseen data, is maximized and the tree size is minimized.