A critical investigation of recall and precision as measures of retrieval system performance
ACM Transactions on Information Systems (TOIS)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
On O(N^4) Algorithm to Contstruct all Vornoi Diagrams for K Nearest Neighbor Searching
Proceedings of the 10th Colloquium on Automata, Languages and Programming
Using Closed Itemsets for Discovering Representative Association Rules
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Fast Discovery of Representative Association Rules
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
A two-dimensional interpolation function for irregularly-spaced data
ACM '68 Proceedings of the 1968 23rd ACM national conference
Efficient rule discovery in a geo-spatial decision support system
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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In this paper, we investigate interpolation methods that are suitable for discovering spatiotemporal association rules for unsampled sites with a focus on drought risk management problem. For drought risk management, raw weather data is collected, converted to various indices, and then mined for association rules. To generate association rules for the 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. We call them pre-order, in-order and post-order interpolation models. The performance of these three models is experimentally evaluated comparing the interpolated association rules with the rules discovered from actual raw data based on two metrics, precision and recall. Our experiments show that the post-order interpolation model provides the highest precision among the three models, and the Kriging method in the pre-order interpolation model presents the highest recall.