# Coverage-Based Classification Using Association Rule Mining +
## 1 Introduction  
Association rule mining [1, 2] is one of the important data mining tasks that is nowadays applied to solve different kinds of problems in weak formalization fields. The main goal of association rule mining is to find all rules in datasets that satisfy some basic requirements, such as minimum support and minimum confidence. It was initially proposed by [PERSON] [3] to solve the market basket problem in transactional datasets and now it has developed to solve many other problems, such as classification, clustering, etc. Classification and prediction are two important tasks of data mining that can be used to extract the hidden regularities from the given datasets to form accurate models (classifiers). Classification approaches aim to build classifiers (models) to predict the class label of a future data object. Such analysis can help to provide us with a better understanding of the data comprehensively. Classification methodshave been widely used in many real-world applications, such as customer relationship management [4], medical diagnosis [5] and industrial design [6]. Associative Classification (AC) is a combination of these two important data mining techniques, namely, classification and association rule mining. AC aims to build accurate and efficient classifiers based on association rules. Researchers have proposed several classification algorithms based on association rules called associative classification methods, such as CBA: Classification-based Association [7], CMAR: Classification based on Multiple Association Rules [8], MCAR: Mining Class Association Rules [9], CPAR: Classification based on Predicted Association Rule [10], MMSCBA: Associative classifier with multiple minimum support [11], CBC: Associative classifier with a small number of rules [12], MMAC: Multi-class, multi-label associative classification [13], ARCID: Association Rule-based Classification for Imbalanced Datasets [14], and others. Experimental evaluations [7; 10; 11; 12; 13; 14; 15] show that associative classification approaches could achieve higher accuracy than some traditional classification algorithms produced by decision trees [16; 17; 18; 19; 20; 21], rule induction [22; 23; 24; 25; 26] and non-rule-based approaches [27; 28; 29; 30]. Most of the associative classification methods [8; 9; 31; 32; 33; 34; 35; 36; 37] tend to build accurate and compact classifiers that include a limited number of effective rules by using rule selection or pruning techniques. In comparison with some traditional rule-based classification approaches, associative classification has two main characteristics. Firstly, it generates a large number of association classification rules. Secondly, support and confidence thresholds are applied to evaluate the significance of classification association rules. However, associative classification has some drawbacks. Firstly, it often generates a very large number of classification association rules in association rule mining that is computationally expensive, especially when the training dataset is large. It takes great effort to select a set of high-quality classification rules from among them. Secondly, the accuracy of associative classification depends on the setting of the minimum support and the minimum confidence constraints, that is, unbalanced datasets may heavily affect the accuracy of the classifiers. Although associative classification has some drawbacks, it can achieve higher accuracy than the rule- and tree-based classification algorithms on certain real-life datasets. Our expected contributions in this research work are:  
* To build a compact, accurate and understandable associative classifier (model) based on dataset coverage. * To solve some of the real-life problems by applying our proposed approach (model). To achieve these goals, we propose a relatively simple and accurate method to produce a compact and understandable classifier by exhaustively searching the entire example space.