Chapter 6 - Trees and rules

Abstract

This chapter explains practical decision tree and rule learning methods, and also considers more advanced approaches for generating association rules. The basic algorithms for learning classification trees and rules presented in Chapter 4, Algorithms: the basic methods, are extended to make them applicable to real-world problems that contain numeric attributes, noise, and missing values. We discuss the seminal C4.5 algorithm for decision tree learning, consider an alternative pruning method implemented in the CART tree learning algorithm, and discuss the incremental reduced-error pruning method for growing and pruning classification rules, leading up to the RIPPER and PART algorithms for rule induction. We also briefly consider rule sets with exceptions. The last section of this chapter switches to unsupervised learning of rule sets by investigating how a special-purpose data structure can be constructed to accelerate the process of finding association rules. More specifically, we consider frequent-pattern trees and how they can be used to efficiently search for frequent item sets.