Philippe Fournier-Viger (Ph.D) is a Canadian researcher, full professor at the Harbin Institute of Technology (Shenzhen, China). Five years after completing his Ph.D., he came to China and became full professor at the Harbin Institute of Technology (Shenzhen), after obtaining a title of national talent from the National Science Foundation of China. He has published more than 290 research papers in refereed international conferences and journals, which have received more than 6400 citations. He is associate editor-in-chief of the Applied Intelligence journal (SCI, Q2) and editor-in-chief of Data Science and Pattern Recognition. He is also the founder of the popular SPMF open-source data mining library, which provides more than 190 algorithms for identifying various types of patterns in data, and has been used in more than 830 papers since 2010. He is editor of the book “High Utility Pattern Mining: Theory, Algorithms and Applications” published by Springer in 2019, and co-organizer of several workshops and special tracks such as the Utility Driven Mining and Learning workshop held at top data mining conferences, KDD 2018, ICDM 2019 and ICDM2020. Website: http://www.philippe-fournier-viger.com
Discovering interesting and useful patterns in symbolic data has been the goal of numerous studies. Several algorithms have been designed to extract patterns from data that meet a set of requirements specified by a user. Although many early research studies in this domain have focused on identifying frequent patterns (e.g. itemsets, episodes, rules), nowadays many other types of interesting patterns have been proposed and more complex data types and pattern types are considered. Mining patterns has applications in many fields as they provide glass-box models that are generally easily interpretable by humans either to understand the data or support decision-making. This talk will first highlight limitations of early work on frequent pattern mining and provide an overview of current problems and state-of-the-art techniques for identifying interesting patterns in symbolic data. Topics that will be discussed include high utility patterns, locally interesting patterns, and periodic patterns. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques will be discussed.