Graham J. Wills

Brief Application Description; Visual Data Mining: Recognizing Telephone Calling Fraud

Human pattern recognition skills are remarkable and in many situations far exceed the ability of automated mining algorithms. By building domain-specific interfaces that present information visually, we can combine human detection with machines far greater computational capacity. We illustrate our ideas by describing a suite of visual interfaces we built for telephone fraud detection.

NicheWorks - Interactive Visualization of Very Large Graphs

The difference between displaying networks with 100–1000 nodes and displaying ones with 10,000–100,000 nodes is not merely quantitative, it is qualitative. Layout algorithms suitable for the former are too slow for the latter, requiring new algorithms or modified (often relaxed) versions of existing algorithms to be invented. The density of nodes and edges displayed per inch of screen real estate requires special visual techniques to filter the graphs and focus attention. A system for investigating and exploring such large, complex data sets needs to be able to display both graph structure and node and edge attributes so that patterns and information hidden in the data can be seen. We describe a tool that addresses these needs, the NicheWorks tool. We describe and comment on the available layout algorithms and the linked views system, and detail an example of the use of NicheWorks for analyzing web sites.

Natural Selection: Interactive Subset Creation

Abstract Visualization is a critical technology for understanding complex, data-rich systems. Effective visualizations make important features of the data immediately recognizable and enable the user to discover interesting and useful results by highlighting patterns. A key element of such systems is the ability to interact with displays of data by selecting a subset for further investigation. This operation is needed for use in linked views systems and in drill-down analysis. It is a common manipulation in many other systems and is as ubiquitous as selecting icons in a desktop graphical user interface (GUI). It is therefore surprising to note that little research has been done on how selection can be implemented. This article addresses this omission, presenting a taxonomy for selection mechanisms and discussing the interactions between branches of the taxonomy.