Recent advances in domain-driven data mining-Reference-Cited by-同舟云学术

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Recent advances in domain-driven data mining

Published:2022-12-27 Issue:1 Volume:15 Page:1-7
ISSN:2364-415X
Container-title:International Journal of Data Science and Analytics
language:en
Short-container-title:Int J Data Sci Anal

Author:

Liu Chuanren,Fakharizadi Ehsan,Xu Tong,Yu Philip S.

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Modeling and Simulation,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s41060-022-00378-1.pdf

Reference55 articles.

1. Alves, G., Amblard, M., Bernier, F., Couceiro, M., Napoli, A.: Reducing unintended bias of ML models on tabular and textual data. In: DSAA, pp. 1–10 (2021)

2. Basak, A., Schmidt, K.M., Mengshoel, O.J.: From data to interpretable models: machine learning for soil moisture forecasting. Int. J. Data Sci. Anal. (2022). https://doi.org/10.1007/s41060-022-00347-8

3. Cao, L.: Domain-driven data mining: challenges and prospects. IEEE Trans. Knowl. Data Eng. 22(6), 755–769 (2010)

4. Cao, L.: Combined mining: analyzing object and pattern relations for discovering and constructing complex yet actionable patterns. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3(2), 140–155 (2013)

5. Cao, L.: Non-iidness learning in behavioral and social data. Comput. J. 57(9), 1358–1370 (2014)

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