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Qian Yuhua, Zhang Mingxing, Cheng Honghong. Association Learning: A New Perspective of Mining Association[J]. Journal of Computer Research and Development, 2020, 57(2): 424-432. DOI: 10.7544/issn1000-1239.2020.20190281
Citation: Qian Yuhua, Zhang Mingxing, Cheng Honghong. Association Learning: A New Perspective of Mining Association[J]. Journal of Computer Research and Development, 2020, 57(2): 424-432. DOI: 10.7544/issn1000-1239.2020.20190281

Association Learning: A New Perspective of Mining Association

Funds: This work was supported by the National Natural Science Foundation of China (61672332), the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi, the Program for the San Jin Young Scholars of Shanxi, and the Overseas Returnee Research Program of Shanxi Province (2017023).
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  • Published Date: January 31, 2020
  • Discovering associations is an important task in big data mining and analysis. Most of the existing mining methods just summarize the associations among data statistically, and cannot learn experience from known data as well as generalize to unseen instances. This paper attempts to explore the associations from learning perspective, and some formal definitions of association learning and relative model concepts are proposed. According to the definitions, a learning data set, namely, the two-class associated image data sets (TAID) are constructed. Then three association discriminators are designed, where associated image convolutional neural network discriminator (AICNN) and associated image LeNet discriminator (AILeNet) are end-to-end learning using softmax function for discrimination, associated image K-nearest neighbor discriminator (AIKNN) based on the associated features extracted by convolutional neural network adopts the K-nearest neighbor algorithm for discrimination. Furthermore, these discriminators are tested on the TAID. The discriminant accuracy of AICNN on an image training set of 90 000 samples and 64×64 size is 0.821 7; AILeNet and AIKNN on 22 500 256×256 images are 0.845 6 and 0.866 4 respectively. These three experiments effectively demonstrate the feasibility of learning the associations in data.

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