Summary: In an inductive database, one can not only query the data stored in the database, but also patterns that are implicitly present in these data. In this chapter, we present an inductive database system in which the query language is traditional SQL. More specifically, we present a system in which the user can query the collection of all possible patterns as if they were stored in traditional relational tables. We show how such tables, or mining views, can be developed for three popular data mining tasks, namely itemset mining, association rule discovery and decision tree learning. To illustrate the interactive and iterative capabilities of our system, we describe a complete data mining scenario that consists in extracting knowledge from real gene expression data, after a pre-processing phase.
For the entire collection see [Zbl 1204.68004].