Seminar của GS Philippe Fournier-Viger về chủ đề Algorithms for discovering interesting and profitable patterns in transaction databases sẽ được tổ chức vào lúc 9g đến 11g sáng thứ tư 21/06/2017 tại phòng I 42, cơ sở 227 Nguyễn Văn Cừ.

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Title: Algorithms for discovering interesting and profitable patterns in transaction databases

Abstract: Data mining is an important subfield of computer science that focuses on analyzing data to discover interesting, unexpected and useful patterns, to support decision-making. A fundamental data mining task is frequent pattern mining. It consists of discovering sets of items (products) frequently purchased in customer transaction databases. Although many algorithms have been published for identifying frequent patterns in transactions, these algorithms ignore important information about customer transactions such as the profit generated by the sale of products and their purchase quantities. Thus, these algorithms may find many frequent patterns that yield a low profit, and miss many rare patterns that are highly profitable. To address these issues, the task of high utility pattern mining has been proposed and has become a hot research topic in recent years. High utility pattern mining consists of discovering highly profitable (or important) sets of items in transaction databases, that is, the sets of products purchased together that yield a high profit. Although high utility pattern mining is desirable, it raises many novel challenges from an algorithmic perspective, as the search space for discovering patterns can be very large. In this talk, I will give an introduction to the problem of high utility pattern mining and explain recent state-of-the-art algorithms and techniques that we have proposed for this problem. The talk will give a good overview of this popular research area of data mining.

Biography: Philippe Fournier-Viger(Ph.D) is a Canadian researcher, full professor at the Harbin Institute of Technology (Shenzhen, China) and adjunct professor at University of Moncton (Moncton, Canada). His research interests include data mining, frequent pattern mining, sequence analysis and prediction, big data, and applications. He has received the title of "Youth 1000 talent" from the National Science Fundation of China. He has published more than 150 research papers in refereed international conferences and journals, which have received 1,000 citations in the last three years.  He is the founder of the popular SPMF open-source data mining library, which has been used in 450 research papers since 2010. He is also editor-in-chief of the Data Mining and Pattern Recognition journal.