Large data resources are ubiquitous in science and business. For these domains, an intuitive view on the data is essential to fully exploit the hidden knowledge. Often, these data can be semantically structured by concepts. Since the determination of concepts requires a thorough analysis of the data, data mining methods have to be applied. Subspace clustering has recently shown to be e ective for this task. Although these methods generate concept-based patterns, the user has to provide domain knowledge to gain reasonable
concepts out of the data.
Our demonstration CoDA (Concept Determination and Analysis) is a tool that supports the user in the final step of concept de nition. More concretely, the user is guided through an iterative, interactive process in which concepts are suggested, analyzed, and potentially rene wed. The core aspect of CoDA is an intuitive, concept-driven presentation
of subspace clusters such that concepts can be visually captured.