# Coverage-Based Classification Using Association Rule Mining +
## 3 Problem Definition and Goals  
Associative classification and association rule discovery are two important tasks of association rule mining. There are some differences between them. First, associative classification is supervised learning where the class value is known, while association rule discovery is unsupervised learning. The second and main difference between association rule discovery and AC is that the rule's consequence in AC can have only the class attribute, however, multiple attribute values are allowed in the rule's consequence to discover association rules. Overfitting prevention is an important task in AC, but it is really not essential in association rule discovery as in AC. Overfitting problems usually occur with imbalanced datasets, noise datasets or very small learning datasets. We can define the building of an accurate and compact classifier by using AC in the following steps.  
Figure 1 shows the general steps used in the AC approach. The first step is computationally expensive because it is similar to the association rule discovery which exhaustively searches the entire example space to discover the frequent itemsets (Step 1). In the second step, we generate strong class association rules from frequent itemsets that satisfy a user-defined minimum confidence threshold. A single rule is represented in AC by implication \(X\to C\), where \(C\) is the consequence (class label) and  
is antecedent (Itemset) of the rule, that is, the highest frequency of class associated with its antecedent \(X\) in the learning dataset. Once the strong class association rules are generated, building an accurate and compact classifier is a straightforward approach, because no support counting or no scanning of the training dataset is required. The most important task is to select the most effective ordered subset of rules to form an intended classifier. In the last step, we measure the quality of the classifier on some independent test datasets.  
We assume that we have given a normal relational table which consists of N examples described by L distinct attributes and all examples are classified into M known classes. Attribute type can be categorical (nominal) or continuous (numeric). Our proposed research supports only categorical (nominal) attributes, therefore, we treat all the attributes uniformly. We map the categorical attribute's values to consecutive positive integers. Numeric attributes are discretized into intervals (bins), and the intervals are also mapped to consecutive positive integers. We use an equal height (frequency) discretization method to discretize the numeric attributes. Discretization methods are not discussed in this work since enough discretization methods are proposed in machine learning literature. Our goals are stated below:  
* Our first goal is to generate the complete set of strong class association rules that satisfy the user-specified minimum support and minimum confidence constraints.
* The second and main goal is to build a simple and accurate classifier by straightforward pruning; more precisely, we select the strong rules that have the contribution to the improvement of overall coverage.
* The third goal is to discuss how overall coverage affects the classification accuracy of our proposed classifier.