Discovering relational patterns across multiple databases

Relational patterns across multiple databases can reveal special pattern relationships hidden inside data collections. Existing research in data mining has made significant efforts in discovering different types of patterns from single or multiple databases, but how to find patterns that have a higher support in database A than in database B with a given support threshold alpha is still an open problem. We propose in this paper DRAMA, a systematic framework for Discovering Relational patterns Across Multiple dAtabases. More specifically, given a series of data collections, we try to discover patterns from different databases with patterns' relationships satisfying the user specified constraints. Our method seeks to build a Hybrid Frequent Pattern tree (HFP-tree) from multiple databases, and mine patterns from the HFP-tree by integrating users' constraints into the pattern mining process.