Abstract
In this paper, we investigate interpolation methods that are suitable for discovering spatio-temporal association rules for unsampled points with an initial focus on drought risk management. For drought risk management, raw weather data is collected, converted to various indices, and then mined for association rules. To generate association rules for unsampled sites, interpolation methods can be applied at any stage of this data mining process. We develop and integrate three interpolation models into our association rule mining algorithm. The performance of these three models is experimentally evaluated comparing interpolated association rules with rules discovered from actual raw data.
This research was supported in part by NSF Digital Government Grant No. EIA-0091530, USDA RMA Grant NO. 02IE08310228, and NSF EPSCOR, Grant No. EPS-0091900.
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Li, D., Deogun, J. (2003). Spatio-Temporal Association Mining for Un-sampled Sites. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_68
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DOI: https://doi.org/10.1007/978-3-540-39592-8_68
Publisher Name: Springer, Berlin, Heidelberg
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