An efficient method for mining association rules based on minimum single constraints

Abstract

Mining association rules with constraints allow us to concentrate on discovering a useful subset instead of the complete set of association rules. With the aim of satisfying the needs of users and improving the efficiency and effectiveness of mining task, many various constraints and mining algorithms have been proposed. In practice, finding rules regarding specific itemsets is of interest. Thus, this paper considers the problem of mining association rules whose left-hand and right-hand sides contain two given itemsets, respectively. In addition, they also have to satisfy two given maximum support and confidence constraints. Applying previous algorithms to solve this problem may encounter disadvantages, such as the generation of many redundant candidates, time-consuming constraint check and the repeated reading of the database when the constraints are changed. The paper proposes an equivalence relation using the closure of itemset to partition the solution set into disjoint equivalence classes and a new, efficient representation of the rules in each class based on the lattice of closed itemsets and their generators. The paper also develops a new algorithm, called MAR-MINSC, to rapidly mine all constrained rules from the lattice instead of mining them directly from the database. Theoretical results are proven to be reliable. Because MAR-MINSC does not meet drawbacks above, in extensive experiments on many databases it obtains the outstanding performance in comparison with some of existing algorithms in mining association rules with the constraints mentioned.