From texts, images, and data to attribute based case representation

Workshop program at the 12th Industrial Conference on Data Mining ICDM 2012 (MLDM 2012) July 13-20, 2012, Berlin/GermanyIn this article we study complex case representations in Case-Based Reasoning. To some degree this is a survey paper. But in addition it gives a unified approach to solving the problems connected with representations mentioned in the title in a way that has not been considered so far. The most popular form to represent cases use attribute-based representations. They allow an easy formulation of similarity measures and retrieval functions. However, in practical applications, case problems and solutions are in the first place given in other ways, e.g. by using texts, images, sensor data or speech data. On this level it is hard to apply reasoning and in particular CBR. This is due to the difficulty to determine similarity measures and retrieval functions. In order to overcome this we introduce a general level structure that allows to bridge the gap between bit-oriented low level and the attribute-oriented high level that is accessible to humans as well as CBR systems. The approach is put in the form of a process model