Abstract

Real life transaction databases usually contain both item information and dimension information. Moreover, taxonomies about items likely exist. Knowledge about multilevel and 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 Apriori-based algorithm, the efficiency of Adaptive Apriori suffers from the multiple database scans. In this thesis, we extend FP-growth to attack the problem of multi-level multidimensional frequent pattern mining. We call our algorithm Ada-FP, which stands for Adaptive FP-growth. The efficiency of our Ada-FP is guaranteed by the high scalability of FP-growth. To increase the effectiveness, our Ada-FP pushes various support constraints into the mining process. First, item taxonomy has been explored. Our Ada-FP can discover both inter-level frequent patterns and intra-level frequent patterns. Second, in our Ada-FP, dimension information has been taken into account. We show that our Ada-FP is more flexible at capturing desired knowledge than previous studies.

Citations