Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming 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 which 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 exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section.

AbstractÐVisualization techniques are ofincreasing importance in exploring and analyzing large amounts ofmultidimensional information. One important class of visualization techniques which is particularly interesting for visualizing very large multidimensional data sets is the class ofthe pixel-oriented techniques. The basic idea ofpixel-oriented visualization techniques is to represent as many data objects as possible on the screen at the same time by mapping each data value to a pixel ofthe screen and arranging the pixels adequately. A number of different pixel-oriented visualization techniques have been proposed in recent years and it has been shown that the techniques are useful for visual data exploration in a number of different application contexts. In this paper, we discuss a number ofissues which are ofhigh importance in developing pixel-oriented visualization techniques. The major goal ofthis article is to provide a formal basis of pixel-oriented visualization techniques and show that the design decisions in developing them can be seen as solutions ofwell-defined optimization problems. This is true for the mapping ofthe data values to colors, the arrangement ofpixels inside the subwindows, the shape ofthe subwindows, and the ordering ofthe dimension subwindows. The paper also discusses the design issues of special variants of pixel-oriented techniques for visualizing large spatial data sets. The optimization functions for the mentioned design decisions are important for the effectiveness of the resulting visualizations. We show this by evaluating the optimization functions and comparing it the results to the visualization obtained in a number of different application. Index TermsÐInformation visualization, visualizing large data sets, visualizing multidimensional and multivariate data, visual data exploration, visual data mining. 1