We present an algorithm for mining association rules in arbitrary relational databases. We define association rules over a simple, but appealing subclass of conjunctive queries, and show that many interesting patterns can be found. We propose an efficient algorithm and a database-oriented implementation in SQL, together with several promising and convincing experimental results.
In this paper we propose a new and elegant approach toward the generalization of frequent itemset mining to the multi-relational case. We define relational itemsets that contain items from several relations, and a support measure that can easily be interpreted based on the key dependencies as defined in the relational scheme. We present an efficient depth-first algorithm, which mines relational itemsets directly from arbitrary relational databases. Several experiments show the practicality and usefulness of the proposed approach. This technical report is an extended version of our work published in [10] 2
Abstract. We present an algorithm for mining frequent queries in arbitrary relational databases, over which functional dependencies are assumed. Building upon previous results, we restrict to the simple, but appealing subclass of simple conjunctive queries. The proposed algorithm makes use of the functional dependencies of the database to optimise the generation of queries and prune redundant queries. Furthermore, our algorithm is capable of detecting previously unknown functional dependencies that hold on the database relations as well as on joins of relations. These detected dependencies are subsequently used to prune redundant queries. We propose an efficient database-oriented implementation of our algorithm using SQL, and provide several promising experimental results.
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