From visual data exploration to visual data mining: a survey <s> PREVIOUS WORK ON VISUALIZATION OF LARGE DATA SETS <s> Visual data mining techniques have proven to be of high value in exploratory data analysis, and they also have a high potential for mining large databases. In this article, we describe and evaluate a new visualization-based approach to mining large databases. The basic idea of our visual data mining techniques is to represent as many data items as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. The major goal of this article is to evaluate our visual data mining techniques and to compare them to other well-known visualization techniques for multidimensional data: the parallel coordinate and stick-figure visualization techniques. For the evaluation of visual data mining techniques, the perception of data properties counts most, while the CPU time and the number of secondary storage accesses are only of secondary importance. In addition to testing the visualization techniques using real data, we developed a testing environment for database visualizations similar to the benchmark approach used for comparing the performance of database systems. The testing environment allows the generation of test data sets with predefined data characteristics which are important for comparing the perceptual abilities of visual data mining techniques. <s> BIB001 </s> From visual data exploration to visual data mining: a survey <s> PREVIOUS WORK ON VISUALIZATION OF LARGE DATA SETS <s> Presents the state of the art in scientific visualization both as an overview for the scientist and as a basic foundation for developers. Topics are: scientific visualization; frameworks and methodologies; and techniques and algorithms. <s> BIB002
The typical approach to producing visualizations from Table  Data is to map data attributes to certain features in the visualization, with the attributes mapped to the spatial axes (X, Y, Z) usually having a dominating effect on the result. Rather than describing particular techniques, for which good surveys are available  ,  , BIB001 , BIB002 (see also  