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Chapter 27: Problem 10

What are classification rules and how are decision trees related to them?

Short Answer

Expert verified
Classification rules are a set of IF-THEN rules used to classify data inputs. Decision Trees, on the other hand, are a structure that represents decisions based on certain attributes. Each path from the root to a leaf in the decision tree can be viewed as a set of classification rules.

Step by step solution

01

Understanding Classification Rules

Classification is a major technique of supervised learning. Its function is to assign a given input (data item) into one of the given set of classes. Classification rules are a set of IF-THEN rules that allow you to classify data. An example can be 'IF the weather is sunny, THEN the person will play cricket.' The 'IF' part is the condition or rule, and the 'THEN' part is the decision accordingly.
02

Understanding Decision Trees

Decision Trees are a type of flowchart structure where each internal node represents a feature or attribute, the branch represents a decision rule, and each leaf node represents the outcome or decision. Decision trees make it easy to visualize data and make decisions based on rules.
03

Linking Classification Rules with Decision Trees

Decision trees can be seen as a set of classification rules. Each path from the root of the tree to a leaf can be transformed into a classification rule by simply combining the decisions along the path to form the rule's condition and assigning the class label of the leaf node as the rule's conclusion.

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Apply the Apriori algorithm to the following data set. $$\begin{array}{ll} \text { Trans ID } & \text { Items Purchased } \\ \hline 101 & \text { milk, bread, eggs } \\ 102 & \text { milk, juice } \\ 103 & \text { juice, butter } \\ 104 & \text { milk, bread, eggs } \\ 105 & \text { coffee, eggs } \\ 106 & \text { coffee } \\ 107 & \text { coffee, juice } \\ 108 & \text { milk, bread, cookies, eggs } \\ 109 & \text { cookies, butter } \\ 110 & \text { milk, bread } \end{array}$$ The set of items is \(\\{\text { milk, bread, cookies, eggs, butter, coffee, juice }\\}\). Use 0.2 for the minimum support value.

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