报告题目：Algorithms to Find Interesting and Interpretable High Utility Patterns in Symbolic Data

报告人：Philippe Fournier-Viger教授

邀请人：郭崇慧 教授

时间：2019年11月22日（周五），15:00-16:00

地点：经济管理学院B220

报告摘要：

Discovering interesting and useful patterns in symbolic data has been the subject of numerous studies. It consists of extracting patterns from data that meet a set of requirements specified by a user. Although early research work in this domain have mainly focused on identifying frequent patterns (e.g. itemsets), nowadays many other types of interesting patterns have been pro-posed 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 state-of-the-art problems and techniques related to identifying interesting patterns in symbol-ic data. Topics that will be discussed include high utility patterns, locally in-teresting patterns, periodic patterns and statistically significant patterns. Last-ly, the SPMF open-source software will be mentioned and opportunities re-lated to the combination of pattern mining techniques with traditional artifi-cial intelligence techniques.

人物简介：

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 Nation-al Science Foundation of China. He has published more than 200 research papers in refereed international conferences and journals, which have received more than 1000 citations in the last year. He is the founder of the popular SPMF open-source data mining library, which provides more than 150 algorithms for identi-fying various types of patterns in data. The SPMF software has been used in more than 630 papers since 2010 for many applications from chemistry, smartphone usage analysis restaurant recommendation to malware detection. He is editor of the book “High Utility Pattern Mining: Theory, Algorithms and Applications” published by Springer in 2019, and co-organizer of the Utility Driven Mining and Learning workshop at KDD 2018 and ICDM 2019. His research interests include data mining, frequent pattern mining, sequence analysis and prediction, big data, and applications. Website: