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A research summary about triadic concept analysis

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Abstract

Triadic concept analysis (TCA) is an extension of formal concept analysis (FCA), which can be applied to machine learning, data mining, information retrieval, and so on. This paper summarizes the current situation and the development tendency of TCA. We introduce TCA in this paper from four aspects: basic notions of triadic concept analysis, triadic implications and triadic association rules, triadic factor analysis and triadic fuzzy concept analysis.

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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 11371014 and 11071281), the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2014JM8306) and the State Scholarship Fund of China.

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Authors and Affiliations

  1. School of Mathematics, Northwest University, Xi’an, 710069, People’s Republic of China

    Ling Wei & Ting Qian

  2. College of Science, Xi’an Polytechnic University, Xi’an, 710048, People’s Republic of China

    Qing Wan

  3. School of Computer Science and Technology, Xidian University, Xi’an, 710071, People’s Republic of China

    Jianjun Qi

Authors
  1. Ling Wei
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  2. Ting Qian
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  3. Qing Wan
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  4. Jianjun Qi
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Correspondence to Ling Wei.

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Wei, L., Qian, T., Wan, Q. et al. A research summary about triadic concept analysis. Int. J. Mach. Learn. & Cyber. 9, 699–712 (2018). https://doi.org/10.1007/s13042-016-0599-7

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