Information Visualization and Visual Data Mining with a Focus on New Graphic Tools Using Both JMP and SAS for Mortgage Fraud
Anders Hasselrot, PhD, Data Mining Manager, Lloyds TSB Daniel Keim, PhD, Professor and Head of the Data Analysis and Visualization Research Group, Department of Computer Science, University of Konstanz Matthias Schaefer, MS, Co-Author, Department of Computer Science, University of Konstanz
Never before in history have data been generated at such high volumes as today. Exploring and analyzing the vast volumes of data becomes increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques that have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques, which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We demonstrate the classification using a few examples, most of them referring to techniques and systems presented in this special issue.