Heuristic Selection of Aggregated Temporal Data for Knowledge Discovery
Autor: | Howard J. Hamilton, Dee Jay Randall |
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Rok vydání: | 1999 |
Předmět: | |
Zdroj: | Multiple Approaches to Intelligent Systems ISBN: 9783540660767 IEA/AIE |
DOI: | 10.1007/978-3-540-48765-4_76 |
Popis: | We introduce techniques for heuristically ranking aggregations of data. We assume that the possible aggregations for each attribute are specified by a domain generalization graph. For temporal attributes containing dates and times, a calendar domain generalization graph is used. A generalization space is defined as the cross product of the domain generalization graphs for the attributes. Coverage filtering, direct-arc normalized correlation, and relative peak ranking are introduced for heuristically ranking the nodes in the generalization space, each of which corresponds to the original data aggregated to a specific level of granularity. |
Databáze: | OpenAIRE |
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