Efficient integration of data mining techniques in database management systems

We propose a new approach for applying data mining techniques, and more particularly supervised machine learning algorithms, to large databases, in acceptable response times. This goal is achieved by integrating these algorithms within a database management system. We are thus only limited by disk capacity, and not by available main memory. However, the disk accesses that are necessary to scan the database induce long response times. Hence, we propose an original method to reduce the size of the learning set by building its contingency table. The machine learning algorithms are then adapted to operate on this contingency table. In order to validate our approach, we implemented the IDS decision tree construction method and showed that using the contingency table helped us obtaining response times equivalent to those of classical, in-memory software.