Parallel algorithms for enumerating closed patterns from multi-relational data

This paper presents parallel algorithms for enumerating closed patterns from multi-relational data. In multi-relational data mining (MRDM), patterns are represented in logical formulae, and involve multiple tables (relations) from a relational database. Since the expressive framework of MRDM makes the task of pattern mining costly compared with the conventional itemset mining, we propose parallel algorithms for computing closed patterns on multi-core processors. In particular, we present new load-balancing strategies which try to fully exploit the task-parallelism intrinsic in the search process of the problem, and give some experimental results, which show the effectiveness of the proposed methods. We then apply our proposed methods to compute closed patterns, a.k.a. concept intents, for binary object-attribute relational data, and show by experiments that the performance of our method is comparable to the existing method.