A framework for integrating methods for decision support; Case-Based Reasoning (CBR) and Data Mining (DM) is outlined. The integration approaches are divided on which method that is considered to be master and which is the slave . A system using Bayesian networks for computing similarity metrics is implemented and compared to a traditional CBR system. The data that are used are taken from a database from the oil industry. The retrieved cases vary greatly between the systems, especially on features that are unspecified in the ``new case''. If many features of the ``new case'' are specified, the new system performs better, according to an evaluation by a database expert.
Otto Haug, Til Paris