報告題目：Three-way Clustering: an Advanced Soft Clustering Approach
日期：2023年6月2日
時間：14:00-16:00 PM
線上會議：騰訊會議：566-477-722
報告人：Jingtao Yao 教授，加拿大里賈納大學(xué)（University of Regina）
主持人：朱萍
報告摘要：
Clustering is a machine learning technique that assigns unlabelled data points into different groups based on similarity of data. However, in many cases, we are unable to confidently assign some data points to particular clusters. Soft clustering introduces a probability of the data point belonging to different clusters. Three-way clustering is a recent development of soft clustering based on three-way decisions. In particular, each data point is assigned a value to represent if it is inside, outside, or partial inside a cluster. There are two types of three-way clustering techniques, namely evaluation-based approaches and operation-based approach. The evaluation-based approaches rely on a membership function to calculate the degree of a data point belonging to a cluster. The operator-based approaches use a pair of operators to construct a three-way cluster from a hard two-way cluster. We will introduce, review, and analysis various three-way clustering techniques in this paper. In addition, history of three-way clustering and future development of three-way clustering will also be discussed.
報告人簡介：
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 and a member of Editorial Boards of various international journals. He is currently the President and a Fellow 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. 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 granular computing, rough sets, data mining, and Web-based support systems. He has over 100 refereed journal articles and conference papers published in these areas and has received over 6,600 citations according to Google Scholar. Dr. Yao has been recognized as a top 90,000 (top 0.98%) scientist across all scientific fields over half century based a new standardized citation metrics developed by scientists led by Stanford University.
