Prof.JingTao YaoUniversity of Regina, Canada Dr. JingTao Yao received a Ph.D. degree from the National University of Singapore. He is currently a Professor with the Department of Computer Science, University of Regina, Canada.
Dr. Yao serves as an Area Editor of International Journal of Approximate Reasoning, Special Section Editor of Cognitive Computation, and a member of Editorial Boards of various international journals. He is currently the Steering Committee Chair, a Fellow, and past President of the International Rough Set Society. He was a member of Canada NSERC Discovery Grant Selection Committees and Evaluation Groups: Computer Science from 2017 to 2020, and 2026. He has been a Chair or a member of the Program Committee of numerous international conferences and has edited many volumes of conference proceedings.
Dr. Yao’s research interests include machine learning, deep learning, federated learning, rough sets, data science, three-way decision, and Web-based support systems. He has over 180 refereed journal articles and conference papers published in these areas and has received about 8,000 citations according to Google Scholar. He has three highly cited papers (top 1%) and one hot paper (top 0.1%) according to Web of Science. Dr. Yao has been recognized as a top 90,000 (top 0.77%) scientist across all scientific fields over half century based a new standardized citation metrics developed by scientists led by Stanford University.
Title: Three-Way Clustering in Machine Learning: Methods and Future Directions Abstract: Clustering is a fundamental machine learning technique that organizes unlabeled data into groups based on similarity. In many real-world scenarios, however, it is difficult to confidently assign certain data points to a specific cluster. To address this issue, soft clustering introduces probabilistic membership, allowing a data point to belong to multiple clusters with different degrees of likelihood.
Three-way clustering is a modern extension of soft clustering inspired by the theory of three-way decisions. Instead of assigning a data point strictly to a cluster or excluding it, three-way clustering introduces a third option representing uncertainty. Specifically, each data point is categorized as belonging inside a cluster, outside a cluster, or in a boundary region indicating partial membership.
Existing three-way clustering methods can generally be divided into two categories: evaluation-based approaches and operator-based approaches. Evaluation-based methods rely on membership functions to estimate the degree to which a data point belongs to a cluster and then determine its three-way assignment. In contrast, operator-based methods construct three-way clusters from traditional hard (two-way) clusters using a pair of operators.
In this talk, we introduce and review representative three-way clustering techniques and analyze their key characteristics. We also briefly discuss the historical development of three-way clustering and outline several promising directions for future research.
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Assoc. Prof. Shaolun HuangTsinghua Shenzhen International Graduate School, China Shao-Lun Huang (Member, IEEE) received the B.S. degree with honor from the Department of Electronic Engineering, National Taiwan University, Taipei, Taiwan, in 2008, and the M.S. and Ph.D. degree from the Department of Electronic Engineering and Computer Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA, in 2010 and 2013. From 2013 to 2016, he was was a Postdoctoral Researcher jointly in the Department of Electrical Engineering with the National Taiwan University and the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Since 2016, he has joined Tsinghua-Berkeley Shenzhen Institute, Shenzhen, China, where he is currently an tenured Associate Professor. His research interests include information theory, communication theory, machine learning, and social networks. |
Assoc. Prof. Ling LiuXidian University, China
Ling Liu (Member, IEEE) received the bachelor’s degree in electronic engineering from Nanjing University in 2008, the master’s degree in electronic engineering from Peking University in 2011, and the Ph.D. degree in electronic engineering from Imperial College London in 2015. Previously, he was with the Huawei Central Research Institute, where he was engaged in research on 5G polar codes. In 2019, he joined Shenzhen University, China, as an Assistant Professor. In 2023, he joined Xidian University, China, where he is currently an Associate Professor with Guangzhou Institute of Technology. He has published more than 30 papers in journals and conferences, such as IEEE Transactions on Information Theory, IEEE Transactions on Communications, ISIT, and ITW. His research interests include information theory, coding theory, lattice codes, and post-quantum cryptography. His recognitions include the IEEE WCSP 2021 Best Paper Award.
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Assoc. Prof. Congduan LiSun Yat-sen University, China Congduan Li (Senior Member, IEEE) received the B.S. degree in electrical engineering from the University of Science and Technology, Beijing, China, in 2008, the M.S. degree in electrical engineering from Northern Arizona University, AZ, USA, in 2011, and the Ph.D. degree in electrical engineering from Drexel University, PA, USA, in 2015. From October 2015 to August 2018, he was a Post-Doctoral Research Fellow with the Institute of Network Coding, The Chinese University of Hong Kong, and the Department of Computer Science, City University of Hong Kong. He is currently an Associate Professor with the School of Electronics and Communication Engineering, Sun Yat-sen University, China. His research interests include networks, such as coding, security, wireless, storage, and caching. |
Assoc. Prof. Pengchao HanGuangdong University of Technology, China Pengchao Han is an Associate Professor at the School of Information Engineering, Guangdong University of Technology. She received her Ph.D. degree from Northeastern University, China, in 2021, supervised by Prof. Lei Guo. She was a visiting scholar at Imperial College London, working with Prof. Kin K. Leung. From 2021 to 2023, she conducted postdoctoral research at The Chinese University of Hong Kong, Shenzhen, in the group of Prof. Jianwei Huang. Her main research interests include mobile communication networks and edge computing, network optimization, distributed learning, and knowledge distillation. She has published more than 40 international academic papers. Among them, she has published 8 journal papers as the first/corresponding author, including in prestigious international journals such as IEEE Transactions on Mobile Computing, IEEE Communications Magazine, and IEEE Internet of Things Journal. She has also published 12 conference papers as the first author, including at IEEE INFOCOM, ICDCS, ICASSP, Globecom, and ICC. She received the Third Prize of the Liaoning Provincial Natural Science Academic Achievement Award in 2016, among other honors. She has served as a Technical Program Committee member for NeurIPS, ICML, and IEEE ICDCS, and has been a long-term reviewer for multiple journals and conferences such as IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Internet of Things Journal, and IEEE International Conference on Communications. |
Assoc. Prof. Zhongjun YangSun Yat-sen University, China Research Interest: Encoding and decoding of Polar codes |
