报告题目(Title): Discovering interesting and interpretable high utility patterns in large databases
报告专家(Speaker): Dr.Philippe Fournier-Viger
Professor at the Harbin Institute of Technology (Shenzhen), China
报告时间(Time)：2020-06-23, 9:00-10:15 am
报告地点(Venue)：西南交通大学犀浦校区，利兹学院X9506
主持人(Chair): 朱焱教授（Prof. ZHU Yan）
内容提要（Outline of the Talk）:
A large amount of data is collected daily by retail and online stores about transactions made by customers. This data can be viewed as symbolic data where items are products purchased by customers. Analyzing customer transactions can reveal interesting patterns that can be used for decision making. A traditional way of discovering patterns in symbolic data is to apply algorithms to discover frequent patterns, which represent sets of values appearing frequently in data (e.g. products frequently purchased together by customers). Although this model has been widely applied and used to analyze data and many other applications, it relies on the unrealistic assumption that a pattern appearing frequently in a database is interesting. But in real-life, other measures of interest are more suitable such as the profit yield by patterns.
To address this issue, a lot of attention has been recently given to the task of discovering high utility patterns. It consists of discovering the sets of items (products or values), which yield a high profit (or have a high importance) when purchased (appearing) together. Although many algorithms have been designed for identifying high utility item sets in transactions, many of those algorithms have important limitations such as not considering the time dimension and finding item sets containing items that are weakly correlated. In this talk, we will discuss the problem of high utility item set mining and extensions that have recently proposed to discover more interesting patterns such as periodic high utility patterns (patterns representing recurring customer behavior that yield a high profit), peak high utility item sets (sets of products that yield a high profit during a specific time period, e.g. Chinese New Year), and the problem of discovering correlated items that yield a high profit. Finally, we will briefly mention other problems related to the discovery of high utility patterns and mention the SPMF data mining library.
报告人简介 (Short Biography of the Speaker):
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 280 research papers in refereed international conferences and journals, which have received more than 5800 citations. He is also associate editor-in chief of the Applied Intelligence journal (Springer, SCI, Q2). He is the founder of the popular SPMF open-source data mining library, which provides more than 170 algorithms for identifying various types of patterns in data. The SPMF software has been used in more than 800 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, ICDM 2019 and ICDM 2020. His research interests include data mining, frequent pattern mining, sequence analysis and prediction, big data, and applications.Website: http://www.philippe-fournier-viger. com
