Abstract
During decision making, we are often confronted by a huge amount of factors. These factors may be either an advantage or a disadvantage to a decision objective. For the purpose of low-risk (high-profit), we must scrutinize the possible behavior of these factors. It is parti- cularly useful to grasp which of the disadvantage factors will rarely occur when the expected advantage factors occur, by using past data. Also, we take into account that there are essential differences between positive and negative association rule mining. Using a pruning algo- rithm we can reduce the search space, however, some pruned itemsets may be useful in the extraction of negative rules.
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© 2002 Springer-Verlag Berlin Heidelberg
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(2002). Negative Association Rule. In: Zhang, C., Zhang, S. (eds) Association Rule Mining. Lecture Notes in Computer Science(), vol 2307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46027-6_3
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DOI: https://doi.org/10.1007/3-540-46027-6_3
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