CAR-Miner: An efficient algorithm for mining class-association rules. Building a high accuracy classifier for classification is a problem in real applications. One high accuracy classifier used for this purpose is based on association rules. In the past, some researches showed that classification based on association rules (or class-association rules – CARs) has higher accuracy than that of other rule-based methods such as ILA and C4.5. However, mining CARs consumes more time because it mines a complete rule set. Therefore, improving the execution time for mining CARs is one of the main problems with this method that needs to be solved. In this paper, we propose a new method for mining class-association rule. Firstly, we design a tree structure for the storage frequent itemsets of datasets. Some theorems for pruning nodes and computing information in the tree are developed after that, and then, based on the theorems, we propose an efficient algorithm for mining CARs. Experimental results show that our approach is more efficient than those used previously. Highlights: We propose the MECR-tree data structure for mining class-association rules. Some theorems for fast joining itemsets and computing supports of rules are developed. An efficient algorithm for mining class-association rules based on the MECR-tree and theorems has been proposed. Our proposal algorithm is always faster than ECR-CARM.