Rule Generation using Decision Trees

A DT is a classification scheme which generates a tree and a set of rules, representing the model of different classes, from a given dataset. As per Hans and Kamber [HK01], DT is a flow chart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test and leaf nodes represent the classes or class distributions. The top most node in a tree is the root node. Figure 1 refers to DT induced for dataset in Table 1. We can easily derive the rules corresponding to the tree by traversing each leaf of the tree starting from the node. It may be noted that many different leaves of the tree may refer to the same class labels, but each leaf refers to a different rule. DTs are attractive in DM as they represent rules which can readily be expressed in natural language. The major strength of the DT methods are the following: