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Negative Association Rule

Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2307)

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.

Keywords

Association Rule Relative Entropy Frequent Itemsets Negative Rule Apriori Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

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