Efficient spatio-temporal data mining with GenSpace graphs
| Autor: | Howard J. Hamilton, Leah Findlater, Dee Jay Randall, Liqiang Geng |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Logic
Computer science Process (engineering) Concept mining 02 engineering and technology Spatial data mining User expectations computer.software_genre Ranking (information retrieval) Knowledge discovery Knowledge extraction 020204 information systems 0202 electrical engineering electronic engineering information engineering Spatio-temporal data mining Data mining Data stream mining Applied Mathematics Temporal data mining Summarization Automatic summarization Domain generalization graphs Path (graph theory) GenSpace graphs 020201 artificial intelligence & image processing computer |
| Zdroj: | Journal of Applied Logic. 4:192-214 |
| ISSN: | 1570-8683 |
| DOI: | 10.1016/j.jal.2005.06.005 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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