Keynote Speech Information-Jerry Chun-Wei Lin
- Professor at the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
- Editor-in-Chief, Data Science and Pattern Recognition (DSPR) Journal
- Fellow, IET
Biography: Jerry Chun-Wei Lin is currently working as the full Professor at the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. He has 100+ papers in IEEE/ACM journals and international conferences. His research interests include data mining and analytics, soft computing, deep learning/machine learning, optimization, IoT applications, and privacy-preserving and security technologies. He is the Editor-in-Chief of Data Science and Pattern Recognition (DSPR) journal, Associate Editor/Editor for 15 SCI journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Dependable and Secure Computing, Information Sciences, among others. He is the leader of the well-known SPMF project, which provides more than 200 data mining algorithms and has been widely cited in many different applications. He has been awarded as the Most Cited Chinese Researcher in 2018, 2019, and 2020 by Elsevier/Scopus and Top-2% Scientist in 2019, 2020, 2021, and 2022 respectively by Stanford University. He is a Fellow of IET (FIET), ACM Distinguished Scientist, and IEEE Senior Member.
Title: Utility-Driven Modeling and Mining Algorithms
Abstract: As a large amount of data is collected daily from individuals, businesses, and other organizations or applications, various algorithms have been developed to identify interesting and useful patterns in data that meet a set of requirements specified by a user. The main purpose of data analysis and data mining is to find new, potentially useful patterns that can be used in real-world applications. For example, analyzing customer transactions in a retail store can reveal interesting patterns about customer buying behavior that can then be used for decision making. In recent years, the demand for utility-oriented pattern mining and analytics has increased because it can discover more useful and interesting information than basic binary-based pattern mining approaches, which has been used in many domains and applications, e.g., cross-marketing, e-commerce, finance, medical and biomedical applications. In this talk, I will first highlight the benefits by using the utility-oriented pattern mining and analytics compared to the past studies (e.g., association rule/frequent itemset mining). I will then provide a general overview of the state of the art in utility-oriented pattern mining and analytic techniques according to three main categories (i.e., data level, constraint level, and application level). Several techniques and modeling on different aspects (levels) of utility-oriented pattern mining will be presented and reviewed.