ABSTRACT
This chapter describes the algorithms used to create the data structures by detailing four fundamental data mining techniques. It focuses on supervised learning by presenting a standard algorithm for creating decision trees. Decision trees are probably the most popular structure for supervised data mining. A common algorithm for building a decision tree selects a subset of instances from the training data to construct an initial tree. The chapter outlines a fundamental covering rule algorithm and it demonstrates an efficient technique for generating association rules. It also focuses on the unsupervised clustering and the K-means algorithm. The chapter shows one how genetic algorithms can perform supervised learning. A genetic approach can oftentimes be used in conjunction with a more traditional technique to improve model performance. Genetic algorithms apply the theory of evolution to inductive learning. Genetic learning can be supervised or unsupervised and is typically used for problems that cannot be solved with traditional techniques.
