MCFPTree: An FP-tree-based algorithm for multi-constraint patterns discovery

In this paper, the problem of constraint-based pattern discovery is investigated. By allowing more user-specified constraints other than traditional rule measurements, e.g., minimum support and minimum confidence, research work on this topic endeavoured to reflect real interest of analysts and relieve them from the overabundance of rules. Surprisingly, very little research has been conducted to deal with multiple types of constraints. In our previous work, we have studied this problem, specifically focusing on three different types of constraints, and an efficient Apriori-like algorithm, called MCFP, is proposed. In this paper, we propose a new algorithm called MCFPTree, which is based on a tree structure for keeping frequent patterns without suffering from the problem of candidate itemsets generation. Experimental results show that our MCFPTree algorithm is significantly faster than MCFP and an intuitive method FP-Growth+, i.e., post-processing the frequent patterns generated by FP-Growth, against user-specified constraints.