Abstract
Indirect association rules and association action rules are two notable extensions of traditional association rules. Since these two extended rules consist of a pair of association rules, they share the same essential drawback of association rules: a huge number of rules will be derived if the target database to be mined is dense or the minimum threshold is set low. One practical approach for alleviating this essential drawback is to rank the rules to identify which one to be examined first in a post-processing. In this paper, as a new application of representation learning, we propose evaluation measures for indirect association rules and association action rules, respectively. The proposed measures are assessed preliminary using a dataset on Japanese video-sharing site and that on nursery.
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Acknowledgements
In this paper, the author used the “Nicovideo dataset” provided by National Institute of Informatics. This work was partially supported by JSPS KAKENHI Grant Number 17K00315.
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Ozaki, T. (2019). Evaluation Measures for Extended Association Rules Based on Distributed Representations. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2019. Advances in Intelligent Systems and Computing, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-15035-8_29
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DOI: https://doi.org/10.1007/978-3-030-15035-8_29
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