Multidimensional frequent pattern mining using association rule based constraints

Knowledge about multi-dimensional frequent patterns is interesting and useful. The classic frequent pattern mining algorithms based on a uniform minimum support, such as Apriori and FP-growth, either miss interesting patterns of low support or suffer from the bottleneck of itemset generation. Other frequent pattern mining algorithms, such as Adaptive Apriori, though taking various supports, focus mining at a single abstraction level. Furthermore, as an Aprioribased algorithm, the efficiency of Adaptive Apriori suffers from the multiple database scans. In this paper, we extend FP-growth to attack the problem of multidimensional frequent pattern mining. The algorithm Ada-FP, which stands for Adaptive FP-growth. The efficiency of the Ada-FP is guaranteed by the high scalability of FP-growth. To increase the effectiveness, the Ada-FP pushes various support constraints into the mining process. We show that the Ada-FP is more flexible at capturing desired knowledge than previous Algorithm.