Welcome Prof. Philippe Fournier-Viger from Harbin Institute of Technology, Shenzhen to be keynote speaker!

日期:2020-02-10 点击量:  201次


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Prof. Philippe Fournier-Viger

Harbin Institute of Technology, Shenzhen, China   


Research Area:

Data Mining, Big Data, Artificial Intelligence, Pattern Mining, Itemset Mining, Graph Mining, Sequence Prediction


Title:  Discovering interesting and interpretable high utility patterns in large databases


Abstract: 

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 itemsets in transactions, many of those algorithms have important limitations such as not considering the time dimension and finding itemsets containing items that are weakly correlated. In this talk, we will discuss the problem of high utility itemset 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 itemsets (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.