Data mining algorithms aim to provide some means to expose the hidden information behind data. But considering a particular problem statement raises the question, which algorithm should be employed and moreover how and which processing steps should be nested to convey a target-aimed knowledge discovery process. Present approaches such as the CRISP-DM are mainly focused at the management or the description of such processes but they do not really describe how such a discovery process should be designed.

In the presented work we propose a framework aiming at the design of a knowledge discovery process where the prior knowledge of a user and his goals are central to the process design. We discuss three stages of knowledge, leading to a framework with three layers. For each level we show the particular meaning and transformation of knowledge, being more specific as we move towards the lower layers of the framework. We demonstrate an approach for supposition-based algorithm recommendation in terms of clustering as an illustration of the framework.