Selecting distinctive attributes for concept learning

This paper presents an innovative approach for learning the distinctive attributes of uncertain objects. The proposed system takes instances, clusters them into different concepts and consequently induces a hierarchy which is used for later classification. We introduce the major steps of the approach using a set of city attributes and further illustrate the applicability for a real world problem, namely the learning of structural concepts of business letters.