Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have