Towards parallel mining of closed patterns from multi-relational data

In multi-relational data mining (MRDM), there have been proposed many methods for searching for patterns that involve multiple tables (relations) from a relational database. In this paper, we consider closed pattern mining from a multi-relational database (MRDB). Closed patterns, a.k.a. concept intents, give the condensed representations of frequent patterns, without losing any information, and they would be of help to discover information on hidden relationship among a given database. Since the computation of MRDM is costly compared with the conventional itemset mining, we propose a parallel algorithm for computing closed patterns on multi-core processors. In particular, we present a new load-balancing strategy which tries 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 method.