Visualization support for user-centered model selection in knowledge discovery in databases

The process of knowledge discovery in databases inherently consists of several steps that are necessarily iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield different discovered models. The selection of appropriate discovered models or algorithms to achieve such models, referred to as model selection-requires meta-knowledge on algorithm/model and model performance metrics - is generally a difficult task for the user. Taking account of this difficulty, we consider that the ease of model selection is crucial in the success of real-life knowledge discovery activities. Different from most related work that aims to an automatic model selection, in our view model selection should be a semiautomatic work requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on such model selection and visualization in the development of a knowledge discovery system called D2MS.