An important issue in data mining is the recognition of complex dependencies between attributes. Past techniques for identifying attribute dependence include correlation coefficients, scatterplots, and equiwidth histograms. These techniques are sensitive to outliers, and often are not sufficiently informative to identify the kind of attribute dependence present. We propose a new approach, which we call independence diagrams. We divide each attribute into ranges; for each pair of attributes, the combination of these ranges defines a two-dimensional grid. For each cell of this grid, we store the number of data items in it. We display the grid, scaling each attribute axis so that the displayed width of a range is proportional to the total number of data items within that range. The brightness of a cell is proportional to the density of data items in it. As a result, both attributes are independently normalized by frequency, ensuring insensitivity to outliers and skew, and ...

If we were to have a Grid infrastructure for visualization, what technologies would be needed to build such an infrastructure, what kind of applications would benefit from it, and what challenges are we facing in order to accomplish this goal? In this survey paper, we make use of the term ‘visual supercomputing ’ to encapsulate a subject domain concerning the infrastructural technology for visualization. We consider a broad range of scientific and technological advances in computer graphics and visualization, which are relevant to visual supercomputing. We identify the state-of-the-art technologies that have prepared us for building such an infrastructure. We examine a collection of applications that would benefit enormously from such an infrastructure, and discuss their technical requirements. We propose a set of challenges that may guide our strategic efforts in the coming years.

Traditional statistical methods deal with car- roborating given hypotheses on a given body of data. However, generating the hypothesis itself is a matter of intuition and ingenuity. It is clem'ly impossible to test all hypotheses on a database with millions of records and hundreds of fields.

This paper addresses the issue of how information visualization techniques can be used to assist full-text search in electronic documents. Our approach supports multiple term queries with interactive treemaps. We use a treemap to visualize the basic structure of the document and exploit color coding to show the distribution of query terms on various levels of the hierarchy. Furthermore, we include filtering techniques to concentrate on those parts of the structure that actually contain the requested information, and, finally provide interactive tools to give access to detailed information whenever the user wishes. Categories and Subject Descriptors (according to ACM CCS): H.5.2 [Information Interfaces and Presentation]: User Interfaces – Graphical user interfaces (GUI) I.3.6 [Computer Graphics]: Methodology and Techniques – Interaction

The availability of large amounts of data in many application domains (e.g., bioinformatics or medical informatics) offers unprecedented opportunities for knowledge discovery in such domains. The classification community has focused primarily on building accurate predictive models from the available data. Highly

KnowledgeMiner" was designed to support the knowledge extraction process on a highly automated level. Implemented are 3 different GMDH-type self-organizing modeling algorithms to make knowledge extraction systematically, fast, successful and easy-to-use even for large and complex system such as one of the most complex systems: the human. Self-organizing data mining technologies in medical data analysis have to select automatically useful knowledge for medical decisions, such as diagnosis of heart disease.

... In this paper, a few of the central concepts of InfoVis are introduced: (1) visualization with multiple views, which often are (but not necessarily need to be) of different visualization types and which are visually linked to each other, especially when used in conjunction with interactive brushing (linking and brushing, L&B); (2) focus-plus-context visualization (F+C visualization) as a means to jointly support zooming into the visual depiction of the data while at the same time maintaining the visual orientation of the visualization user to support navigation in the visualization; and (3) the potential combination of visualization methods and such from statistics as an interesting perspective for future work.

Traditional statistical methods deal with car- roborating given hypotheses on a given body of data. However, generating the hypothesis itself is a matter of intuition and ingenuity. It is clem'ly impossible to test all hypotheses on a database with millions of records and hundreds of fields.

If we were to have a Grid infrastructure for visualization, what technologies would be needed to build such an infrastructure, what kind of applications would benefit from it, and what challenges are we facing in order to accomplish this goal? In this report, we make use of the term ‘visual supercomputing ’ to encapsulate a subject domain concerning the infrastructural technology for visualization. We consider a broad range of scientific and technological advances in computer graphics and visualization, which are relevant to visual supercomputing. We identify the state of the art technologies that have prepared us for building such an infrastructure. We examine a collection of applications that would benefit enormously from such an infrastructure, and discuss their technical requirements. We propose a set of challenges that may guide our strategic efforts in the coming years.