The Upcoming 3rd Utility Mining and Learning Workshop (UDML 2020!) at IEEE ICDM 2020

Today, I just want to talk a little bit about the upcoming 3rd UDML 2020 workshop on utility mining and learning that I am co-organizing at the IEEE ICDM 2020 conference. This workshop was previously held at KDD 2018 and ICDM 2019.

This year, we have received several papers. All papers have been reviewed by at least 3 reviewers. The final set of papers that has been accepted is:

  • Insights From Urban Sensing Data: From Chaos to Predicted Congestion Patterns
    Minh-Son Dao, Ngoc-Thanh Nguyen, Rage Uday Kiran, and Koji Zettsu
  • Efficient Mining of Non-Dominated High Quantity-Utility Patterns
    Jimmy Ming-Tai Wu, Qian Teng, Gautam Srivastava, Matin Pirouz, and Jerry Chun-Wei Lin
  • A Tree-based Fuzzy Average-Utility Mining Algorithm
    Tzung-Pei Hong, Meng-Ping Ku, Wei-Ming Huang, Shu-Min Li, and Chun-Wei Lin
  • Sample-Rank: WeakMulti-Objective Recommendations Using Rejection Sampling
    Abhay Shukla, Jairaj Sathyanarayana, and Dipyaman Banerjee
  • Valuing Player Actions in Counter-Strike: Global Offensive
    Peter Xenopoulos, Harish Doraiswamy, and Claudio Silva
  • TKC: Mining Top-K Cross-Level High Utility Itemsets
    Mourad Nouioua, Ying Wang, Philippe Fournier-Viger, Jerry Chun-Wei Lin, and Jimmy Ming-Tai Wu

Some papers are related to pattern mining, as it is one of the main theme of this workshop. But it is interesting that some good papers on other topics have also been accepted. For example, there is a paper about evaluating the value of players in Counter-Strike. In that context, the concept of utility has a special meaning (value of a player). Also, this year, some papers focus a bit more on application such as the one about using pattern mining for studying congestion data.

The workshop will be held on November 17th online. The proceedings of the workshop should be published by IEEE at approximately the same time.


Philippe Fournier-Viger is a computer science professor and founder of the SPMF open-source data mining library, which offers more than 170 algorithms for analyzing data, implemented in Java.

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