Abstract
For each implication \(\tau : Condition\_part\Rightarrow Decision\_part\) defined in table data sets, we see \(\tau \) is a rule if \(\tau \) satisfies appropriate constraints, i.e., \(support(\tau )\ge \alpha \) and \(accuracy(\tau )\ge \beta \) for two threshold values \(\alpha \) and \(\beta \) (\(0<\alpha , \beta \le 1\)). If \(\tau \) is a rule for relatively high \(\alpha \), we say \(\tau \) is supported by major instances. On the other hand, if \(\tau \) is a rule for lower \(\alpha \), we say \(\tau \) is supported by minor instances. This paper focuses on rules supported by minor instances, and clarifies some problems. Then, the NIS-Apriori algorithm, which was proposed for handling rules supported by major instances from tables with information incompleteness, is extended to the NIS-Apriori algorithm with a target descriptor. The effectiveness of the new algorithm is examined by some experiments.
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Sakai, H., Shen, KY., Nakata, M. (2019). NIS-Apriori Algorithm with a Target Descriptor for Handling Rules Supported by Minor Instances. In: Seki, H., Nguyen, C., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2019. Lecture Notes in Computer Science(), vol 11471. Springer, Cham. https://doi.org/10.1007/978-3-030-14815-7_21
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