International Workshop on Domain-driven Data Mining (DDM)

Indeed, domain-driven data mining had attracted significant attention in the literature. In the prominent TKDE 2010 paper “Domain-driven data mining: Challenges and prospects” and the ICDM 2008 workshop on domain-driven data mining, both contributed by Dr. Longbing Cao, thorough efforts focused on promoting actionable knowledge discovery in complex real-world decision making tasks. This workshop aims to foster further discussions on the incorporation of domain knowledge into data mining processes and models, especially methodologies and tools newly developed in the last decade, such as deep neural networks, graph embedding, text mining, and reinforcement learning.

This workshop is currently under review to be part of SDM 2021, a virtual conference, April 29-May 1, 2021. Domain-driven data mining is an important research in SDM given the highly applied and interdisciplinary nature of the conference. In order for data mining algorithms to achieve superior performance in significant business and societal problems, it is necessary to incorporate domain knowledge to guide the model and algorithm design. In the past few years, there are numerous papers published in SDM on the topic, such as domain specific clustering [2], user guided cross-domain sentiment classification [5], multi-domain learning for drug interaction prediction [1], domain-driven model for disease prediction [3], and cross-domain data mining [4,6,7].

Topics of Interest

This workshop aims to bring together leading researchers and practitioners to share their experiences and latest research/application results on all aspects of Domain-driven Data Mining. The topics of interest include but not limited to:

  • Domain Knowledge guided Data Pre-processing
  • Mining of Domain-specific Terminologies
  • Domain-driven Knowledge Graph 
  • Modeling for Domain-specific Features/Theories 
  • Domain-driven Multi-modal Data Mining
  • Evaluation Strategy with Domain Expertise
  • Solution for Domain-driven Applications
  • Cross-domain/Multi-domain Learning

Program Committee

  • Yao Hu, Senior Researcher, Alibaba Youku Cognitive and Intelligent Lab 
  • Yanhua Li, Worcester Polytechnic Institute
  • Xiangwen Liao, Professor, Fuzhou University
  • Jingyuan Wang, Associate Professor, Beihang University
  • Xiangyu Zhao, Research Assistant, Michigan State University
  • Hao Zhong, Assistant Professor, ESCP
  • Xun Zhou, University of Iowa
  • Hengshu Zhu, Research Scientist, Baidu Inc.

Submission/Attendance Instructions

SDM is a leading conference in the field of data mining for researchers and practitioners all over the world. Data mining with integration of domain knowledge has become an increasingly crucial research focus for both academia and industry.

Important Dates: paper submission: March 1, 2020; paper notification: April 1, 2020.

Submission (paper/abstract/demo): link to be available soon.

Biography of Organizers

Yanjie Fu, University of Central Florida

Contact Information: 4000 Central Florida Blvd. Orlando, Florida, 32816


Dr. Yanjie Fu is an assistant professor in the Department of Computer Science at the University of Central Florida. He received his Ph.D. degree from Rutgers, the State University of New Jersey in 2016, the B.E. degree from University of Science and Technology of China in 2008, and the M.E. degree from Chinese Academy of Sciences in 2011. His research interests include data mining and big data analytics. He has research experience in industry research labs, such as Microsoft Research Asia and IBM Thomas J. Watson Research Center. He has published prolifically in refereed journals and conference proceedings, such as IEEE TKDE, ACM TKDD, IEEE TMC, ACM TIST, ACM SIGKDD, AAAI, and IJCAI.


  1. R. Cai, Z. Zhang, S. Parthasarathy, A. K. Tung, Z. Hao, and W. Zhang. Multi-domain manifold learning for drug-target interaction prediction. In Proceedings of the 2016 SIAM International Conference on Data Mining, pages 18–26. SIAM, 2016.
  2. Y. Chang, J. Chen, M. H. Cho, P. J. Castaidi, E. K. Silverman, and J. G. Dy. Clustering with domain-specific usefulness scores. In Proceedings of the 2017 SIAM International Conference on Data Mining, pages 207–215. SIAM, 2017.
  3. Y. Lin, K. Liu, E. Byon, X. Qian, and S. Huang. Domain-knowledge driven cognitive degradation modeling for alzheimer’s disease. In Proceedings of the 2015 SIAM International Conference on Data Mining, pages 721–729. SIAM, 2015.
  4. Z. Lu, E. Zhong, L. Zhao, E. W. Xiang, W. Pan, and Q. Yang. Selective transfer learning for cross domain recommendation. In Proceedings of the 2013 SIAM International Conference on Data Mining, pages 641–649. SIAM, 2013.
  5. A. R. Nelakurthi, H. Tong, R. Maciejewski, N. Bliss, and J. He. User-guided cross-domain sentiment classification. In Proceedings of the 2017 SIAM International Conference on Data Mining, pages 471–479. SIAM, 2017.
  6. G.-J. Qi, C. Aggarwal, and T. Huang. Transfer learning of distance metrics by cross-domain metric sampling across heterogeneous spaces. In Proceedings of the 2012 SIAM International Conference on Data Mining, pages 528–539. SIAM, 2012.
  7. F. Zhuang, P. Luo, H. Xiong, Q. He, Y. Xiong, and Z. Shi. Exploiting associations between word clusters and document classes for cross-domain text categorization. Statistical Analysis and Data Mining: The ASA Data Science Journal, 4(1):100–114, 2011.