Towards efficient closed pattern mining from distributed multi-relational data

We consider closed pattern mining from distributed multi-relational databases, especially focusing on its efficient implementation. Given a set of local databases (horizontal partitions), we first compute their sets of closed patterns (concepts) using a closed pattern mining algorithm tailored to multi-relational data mining (MRDM). We then generate the set of closed patterns in the global database by utilizing the merge (or subposition) operator, studied in the field of Formal Concept Analysis. Since the computational complexity of MRDM increases compared with the conventional itemset mining, we propose some methods for improving the overall computations. We also present some experimental results using a distributed computation environment based on the MapReduce framework, which shows the effectiveness of the proposed methods.