Abstract
Association rule mining has become an important component of information processing systems due to significant increase in its applications. In this paper, our main objective is to find which interpolation approaches are best suited for discovering geo-spatial association rules from unsampled points. We investigate and integrate two interpolation approaches into our geo-spatial association rule mining algorithm. We call them pre-interpolation and post-interpolation approaches.
This research was supported in part by NSF Digital Government Grant No. EIA-0091530 and NSF EPSCOR, Grant No. EPS-0091900.
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Li, D., Deogun, J., Harms, S. (2003). Interpolation Techniques for Geo-spatial Association Rule Mining. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_93
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DOI: https://doi.org/10.1007/3-540-39205-X_93
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