Mining concise Association Rules based on generators and closed itemsets

It is well-recognized that the main factor that hinders the applications of association rules (ARs) is the huge number of ARs returned by the mining process. To solve this problem, an algorithm for mining concise association rules based on generators and closed itemsets is proposed. Firstly, the concept of concise association rule is proposed, and the rationality of the definition is explained based on conviction. Then, the definitions of concise min-max precise rule basis and concise min-max approximate rule basis are proposed, and the corresponding pruning strategies are discussed. Finally, the characteristics and connection strategies of generator are presented, and based on subsume index, a breadth-first algorithm for mining concise association rule is proposed. Experimental results show that the concise rules with smaller sizes can be discovered. Thus, the understandability of mining result is improved.