## 2 Literature Review
A vertical data format [34] was utilized to generate the rule items associated with their intersection IDs. The support and confidence values of the CARs were computed based on the intersection technique. Once the potential CAR is found for the classifier, the associated transaction will be discarded by using a set difference to eliminate generating redundant CARs. This is a nice related work that differs from our method in the rule selection stage. More precisely, any clustering technique was used in the rule extraction phase of the proposed model. The distance-based clustering approach [35] aims to cluster the association rules generated from numeric attributes. They followed the same process to cluster and select the representative rule for each cluster as in our algorithm. The steps are similar, but the methods used in each step are different. (1) They used the \"APRIORI\" algorithm to find the association rules from the dataset with numeric attributes; (2) since they are working with numeric attributes, the Euclidean distance metric is used to find similarities between association rules; (3) a representative rule is selected based on coverage, which measures the degree of a certain rule to cover all others. In [36], researchers proposed a new similarity measure based on the association rule for clustering gene data. They first introduced a feature extraction approach based on statistical impurity measures, such as the Gini Index and Max Minority, and they selected the top 100-400 genes based on that approach. Associative dependencies between genes are then analyzed, and weights to the genes are assigned according to their frequency of occurrences in the rules. Finally, a weighted Jaccard and vector cosine similarity functions are presented to compute the similarity between the generated rules, and the produced similarity measures are applied later to cluster the rules by utilizing the hierarchical clustering algorithm. In this approach, some steps are similar to our method, but different techniques are used in those steps. In [37], researchers proposed a novel distance metric to measure the similarity of association rules. The main goal of the research was to mine clusters with association rules. They first generated the association rules by using the \"APRIORI\" algorithm, one of the most-used algorithms. They introduced an \"Hamming\" distance function (based on coverage probabilities) to cluster (a hierarchical clustering algorithm is used) the rules. The key difference between our method and the proposed method is that this study aimed to produce a compact and accurate associative classifier, while its main goal was to measure the quality of the clustering. In [38], the authors focused on detecting unexpected association rules from transactional datasets. They proposed a method for generating unexpected patterns based on beliefs automatically derived from the data. They clustered the association rules by integrating a density-based clustering algorithm. Features are represented as vectors captured by semantic and lexical relationships between patterns. The clustering phase considers such logical relationships as similarities or distances between association rules. The idea is slightly similar to ours, but used a different clustering technique and cluster association rules, not class association rules.