In later years there has been a growing interest in mining frequent itemsets by applications tightly-integrated with a database. Researchers have also begun to study inductive databases, a new generation of databases for leveraging decision support applications. In this context, the user interacts with the database management system using advanced, constraint-based languages for data mining where constraints have been specifically introduced to increase the relevance of the results and at the same time reduce its volume.
In this paper we study the problem of mining frequent itemsets by an inductive database. We propose a technique of query answering that is based on materialization of previously executed queries. We present conditions for query optimization by means of query rewriting and composition of the materialized queries. We will see that these conditions are strictly connected with the presence of functional dependencies between the attributes involved in the queries. We show some experiments on an initial prototype of an inductive database which demonstrate that this approach to query answering is not only viable but in many practical cases absolutely necessary since it reduces dramatically the high execution times of data mining queries.