Abstract
As an effective tool for analyzing human behavior and social cognition, rule extraction is a key issue in cognitive computing. However, the existing association rules state whether an attribute is possessed only without considering the order relation of attribute values. In this paper, we propose a novel method for quantitative association rule mining based on a generalized one-sided context and solid cognitive foundations. The extracted rule indicates the order relation of the attribute values and the structure of truth values for different attributes. We also propose specific algorithms to extract generalized one-sided quantitative association rules and non-redundant generalized one-sided quantitative association rules. The scale of data also needs to be considered by cognitive computing. An object may possess different values for the same attribute according to different measuring scales. The relationship between generalized one-sided quantitative association rules at different scales is also discussed. Rather than converting the multi-valued formal context into a binary formal context, the generalized one-sided quantitative association rule is extracted directly in a multi-valued formal context. The experimental results show the presented algorithms reduce both time and space complexity compared with the classical quantitative association rule-mining algorithm.
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This work was supported by the National Natural Science Foundation of China(Nos.62076088)
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Hu, Z., Shao, M., Liu, H. et al. Cognitive Computing and Rule Extraction in Generalized One-sided Formal Contexts. Cogn Comput 14, 2087–2107 (2022). https://doi.org/10.1007/s12559-021-09868-z
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DOI: https://doi.org/10.1007/s12559-021-09868-z