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CFP: IEEE Access special issue on Utility Pattern Mining - Deadline: 31 January 2020
Posted by:
webmasterphilfv
Date: August 14, 2019 07:05PM
Hi all,
I am glad to announced that we are organizing a special issue in the IEEE Access journal on utility pattern mining. The CFP is below.
Submission Deadline: 31 January 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:
Philippe Fournier-Viger, Harbin Institute of Technology (Shenzhen), China
Vincent S. Tseng, National Chiao Tung University, Taiwan
Philip Yu, University of Illinois at Chicago, USA
Edited 1 time(s). Last edit at 08/14/2019 07:06PM by webmasterphilfv.
I am glad to announced that we are organizing a special issue in the IEEE Access journal on utility pattern mining. The CFP is below.
Submission Deadline: 31 January 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:
Philippe Fournier-Viger, Harbin Institute of Technology (Shenzhen), China
Vincent S. Tseng, National Chiao Tung University, Taiwan
Philip Yu, University of Illinois at Chicago, USA
Edited 1 time(s). Last edit at 08/14/2019 07:06PM by webmasterphilfv.