Abstract
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.
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© 1999 Springer-Verlag Berlin Heidelberg
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Hamilton, H.J., Randall, D.J. (1999). Heuristic Selection of Aggregated Temporal Data for Knowledge Discovery. In: Imam, I., Kodratoff, Y., El-Dessouki, A., Ali, M. (eds) Multiple Approaches to Intelligent Systems. IEA/AIE 1999. Lecture Notes in Computer Science(), vol 1611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48765-4_76
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DOI: https://doi.org/10.1007/978-3-540-48765-4_76
Publisher Name: Springer, Berlin, Heidelberg
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