Utility Pattern Mining: Theoretical Analytics and Applications

Submission Deadline: 30 April 2020

IEEE Access invites manuscript submissions in the area of Utility Pattern Mining: Theoretical Analytics and Applications.

Utility pattern mining from data has received a lot of attention from the Knowledge Discovery in Data Mining (KDD) community due to the high potential impact in many applications such as finance, biomedicine, manufacturing, e-commerce and social media. Current research in utility mining primarily focuses on discovering patterns of high value (e.g. high profit) in large databases, and analyzing/learning important factors (e.g. economic factors) in a data mining process. One of the popular applications of utility mining is the analysis of large transactional databases to discover high-utility itemsets, which consist of sets of items that generate a high profit when purchased together.

This Special Section in IEEE Access aims at bringing together academic and industrial researchers and practitioners from data mining, machine learning and other interdisciplinary communities, in a collaborative effort to identify and discuss major technical challenges, recent results and potential topics on the emerging fields of Utility-Pattern Mining, by focusing especially on theoretical analytics and applications. Studies about real-world experiences, inherent challenges, and new research methods/applications are also welcome.

The topics of interest include, but are not limited to:

  • Theory, applications, and core methods for utility mining and computing
  • Utility patterns mining in large datasets, e.g., high-utility itemset mining, high-utility sequential pattern/rule mining, high-utility episode mining, and other novel patterns
  • Analysing and learning utility factors of mining and learning processes
  • Predictive modeling/learning, clustering and link analysis that incorporate utility factors
  • Incremental utility mining and computing
  • Utility mining and learning in streams
  • Utility mining and learning in uncertain systems
  • Utility mining and learning in big data
  • Knowledge representations for utility patterns
  • Privacy preserving utility mining/learning
  • Visualization techniques for utility mining/computing
  • Open-source software/libraries/platform
  • Innovative applications in interdisciplinary domains like finance, biomedicine, healthcare, manufacturing, e-commerce, social media, education, etc.
  • New, open, or unsolved problems in utility-pattern mining and computation


This Special Section is also published in cooperation with the second “Utility-Driven Mining and Learning (UDML)” workshop held at IEEE ICDM 2019. Important articles from the UDML workshop will be invited for this Special Section, provided that each article has less than 35% similarity to the author’s previous work.

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.


Associate Editor:  Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway

Guest Editors:

    1. Philippe Fournier-Viger, Harbin Institute of Technology (Shenzhen), China
    2. Vincent S. Tseng, National Chiao Tung University, Taiwan
    3. Philip Yu, University of Illinois at Chicago, USA


Relevant IEEE Access Special Sections:

  1. Innovation and Application of Intelligent Processing of Data, Information and Knowledge as Resources in Edge Computing
  2. Data-Enabled Intelligence for Digital Health
  3. Data Mining for Internet of Things

IEEE Access Editor-in-Chief:
  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact:  jerrylin@ieee.org.