Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4663252 | Journal of Applied Logic | 2006 | 23 Pages |
Abstract
We describe a method for spatio-temporal data mining based on GenSpace graphs. Using familiar calendar and geographical concepts, such as workdays, weeks, climatic regions, and countries, spatio-temporal data can be aggregated into summaries in many ways. We automatically search for a summary with a distribution that is anomalous, i.e., far from user expectations. We repeatedly ranking possible summaries according to current expectations, and then allow the user to adjust these expectations. We also choose a propagation path in the GenSpace subgraph that reduces the storage and time costs of the mining process.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Logic
Authors
Howard J. Hamilton, Liqiang Geng, Leah Findlater, Dee Jay Randall,