Research on Data Mining Techniques Based on DeepSeek-R1

Lei Cai, Fan Yin, Xianbo Meng
Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, PMLR 278:440-452, 2025.

Abstract

With the rapid development of artificial intelligence technology, data mining, as one of its important application fields, is facing new opportunities and challenges. DeepSeek-R1, as an advanced pre-trained model, provides new technical means for data mining with its powerful feature extraction capabilities and efficient inference performance. This paper systematically investigates data mining techniques based on DeepSeek-R1, offering a comprehensive exploration of technical principles, application methods, and performance optimization. Experimental results demonstrate that DeepSeek-R1 exhibits significant performance advantages in data mining tasks, and corresponding optimization strategies are proposed. The research in this paper not only enriches the theoretical system of data mining technology but also provides valuable references for practical applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v278-cai25a, title = {Research on Data Mining Techniques Based on DeepSeek-R1}, author = {Cai, Lei and Yin, Fan and Meng, Xianbo}, booktitle = {Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing}, pages = {440--452}, year = {2025}, editor = {Zeng, Nianyin and Pachori, Ram Bilas and Wang, Dongshu}, volume = {278}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v278/main/assets/cai25a/cai25a.pdf}, url = {https://proceedings.mlr.press/v278/cai25a.html}, abstract = {With the rapid development of artificial intelligence technology, data mining, as one of its important application fields, is facing new opportunities and challenges. DeepSeek-R1, as an advanced pre-trained model, provides new technical means for data mining with its powerful feature extraction capabilities and efficient inference performance. This paper systematically investigates data mining techniques based on DeepSeek-R1, offering a comprehensive exploration of technical principles, application methods, and performance optimization. Experimental results demonstrate that DeepSeek-R1 exhibits significant performance advantages in data mining tasks, and corresponding optimization strategies are proposed. The research in this paper not only enriches the theoretical system of data mining technology but also provides valuable references for practical applications.} }
Endnote
%0 Conference Paper %T Research on Data Mining Techniques Based on DeepSeek-R1 %A Lei Cai %A Fan Yin %A Xianbo Meng %B Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2025 %E Nianyin Zeng %E Ram Bilas Pachori %E Dongshu Wang %F pmlr-v278-cai25a %I PMLR %P 440--452 %U https://proceedings.mlr.press/v278/cai25a.html %V 278 %X With the rapid development of artificial intelligence technology, data mining, as one of its important application fields, is facing new opportunities and challenges. DeepSeek-R1, as an advanced pre-trained model, provides new technical means for data mining with its powerful feature extraction capabilities and efficient inference performance. This paper systematically investigates data mining techniques based on DeepSeek-R1, offering a comprehensive exploration of technical principles, application methods, and performance optimization. Experimental results demonstrate that DeepSeek-R1 exhibits significant performance advantages in data mining tasks, and corresponding optimization strategies are proposed. The research in this paper not only enriches the theoretical system of data mining technology but also provides valuable references for practical applications.
APA
Cai, L., Yin, F. & Meng, X.. (2025). Research on Data Mining Techniques Based on DeepSeek-R1. Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 278:440-452 Available from https://proceedings.mlr.press/v278/cai25a.html.

Related Material