Data and knowledge visualization with virtual reality spaces, neural networks and rough sets: Application to cancer and geophysical prospecting data

Visual data mining with virtual reality spaces is used for the representation of data and symbolic knowledge. High quality structure-preserving and maximally discriminative visual representations can be obtained using a combination of neural networks (SAMANN and NDA) and rough sets techniques, so that a proper subsequent analysis can be made. The approach is illustrated with two types of data: for gene expression cancer data, an improvement in classification performance with respect to the original spaces was obtained; for geophysical prospecting data for cave detection, a cavity was successfully predicted.

► Virtual reality is used for the representation of data and symbolic knowledge. ► High quality structure-preserving representations are obtained. ► A combination of neural networks and rough sets techniques is used. ► For gene expression cancer data, classification performance improves. ► For geophysical prospecting data, a cavity was successfully predicted.