## 2 Literature Review  
In the computer science literature, rule-based and tree-based classification models have been studied by many scholars. Since this study proposes an associative classification method based on clustering, this section discusses the research work performed in the associative classification field related to the proposed approaches. The \"APRIORI\" algorithm is executed first to create CARs in the CBA approach, which employs the vertical mining method. Then, to generate predictive rules, the algorithm applies greedy pruning based on database coverage. CBA uses a different rule-selection procedure than we do--the rule that can classify at least one training example; that is, if the body and class labels of the candidate rule match those of the training examples, the body and class labels are chosen for the classifier. Because we attempted to decrease the size of the classifier, we utilized clustering first and then chose the representative rule for each cluster. The Simple Associative Classifier (SA) developed a relatively simple classification model (SA) based on association rules. A simple associative classifier was presented by selecting a resealable number of class association rules for each class. The algorithm finds all the rules in the dataset and sorts them based on support and confidence measures. Then, the strong CARs are grouped according to class label, and finally, the user-specified (intended number of CARs) number of CARs for each class is extracted to build a simple associative classifier. In J&B method, a thorough search of the entire example space yielded a descriptive and accurate classifier (J&B). To be more specific, CARs are first produced using the APRIORI method. The strong class association rules are then chosen based on how well they contribute to enhancing the overall coverage of the learning set. In the rule selection process, J&B has a halting condition based on the coverage of the training dataset. If it satisfies the user-defined threshold (intended dataset's coverage), it stops the rule-selection process and forms the final classifier. The algorithm described in this paper extends our previous work [30,31]. In [30], the CMAC algorithm was introduced; it first generates the CARs by employing the APRIORI algorithm; secondly, the algorithm uses a direct distance metric in the clustering phase of CARs; finally, the cluster centroid approach is applied to select the representative CAR for each cluster, while in [31] CMAC is compared to two similar algorithms, one (DDC) using the same direct distance metric for clustering and covering approach in the representative CAR selection phase; the other algorithm (CDC) using combined (direct and indirect) distance metric with the same covering approach to select the representative CAR. This paper presents a similar approach using a combined distance metric (three different metrics are proposed by considering the contribution of direct and indirect measures) in the CAR clustering phase after the CARs are found by using the APRIORI algorithm, and the cluster centroid approach is used to select the representative CAR for each cluster. [PERSON] et al. [32] discovered hidden regularities between binary attributes in large datasets. The authors used similar techniques as in our research: clustering and association rule mining to reduce the number of Ars produced, but the proposed algorithm was totally different. Since there were 3000 attributes in the dataset, their main goal was to cluster (by using the hierarchical clustering algorithm) the attributes to reveal interesting relations between binary attributes and to further reduce the future space. Using the APRIORI algorithm, strong meaningful ARs were generated in the clustered dataset, which can be used for further classification purposes. In [33], the authors developed a new algorithm based on strong class association rules, which obtained 100% confidence. They directly produced CARs with higher confidence to build a compact and accurate model.