ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (2): 424-432.doi: 10.7544/issn1000-1239.2020.20190281

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Association Learning: A New Perspective of Mining Association

Qian Yuhua, Zhang Mingxing, and Cheng Honghong   

  1. (Research Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006) (Key Laboratory of Computational Intelligence and Chinese Information Processing (Shanxi University), Ministry of Education, Taiyuan 030006) (School of Computer and Information Technology, Shanxi University, Taiyuan 030006)
  • Online:2020-02-01
  • Supported by: 
    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).

Abstract: 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.

Key words: association, association learning, association discriminator, association image data sets, association learning criteria

CLC Number: