| Publisher | University College Dublin | ||
|---|---|---|---|
| Format | 270.3KB PDF | Date added | 01 Aug 2006 |
| Topics | Data Mining - Analysis, Data Visualization | ||
| Downloads | 17 | ||
Many natural phenomena present intrinsic spatial and temporal characteristics. With the recent advances in data collection technologies, high resolution spatio-temporal datasets can be stored and analyzed to accurately study the behavior of such events. However, these datasets are often very large and difficult to analyze and display. Recently much attention has been dedicated to the application of innovative data-mining techniques to filter out relevant subsets of very large repositories as well as to the development of visualization tools to effectively display the corresponding results. This paper describes the approach to deal with very large spatio-temporal datasets. The framework includes new techniques to efficiently support the data-mining process, address the spatial and temporal dimensions of the dataset, and visualize and interpret results.
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