Data mining involves exploring databases to try and discover data relationships which are not explicitly stored within the databases.
Traditional techniques involve statistical analysis, clustering, and pattern matching.
Many current efforts are underway to integrate visualization into this process. This project involves examining the benefits that would result from using multivariate data visualization in conjunction with analytic methods to explore databases. This project involves the development of benchmarks for comparing the effectiveness of visualization techniques and running experiments with human subjects to assess the quality of the benchmarks. A current NSF grant is focused on extending the visualization techniques in XmdvTool to handle very large, hierarchical data sets. It supports numerous ways of visualizing the evolution over time via animation and flow visualization, and provides numerous ways of controlling the process.