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2018-03-28 13:24:29
2018-03-28
Website: http://www.philippe-fournier-viger.com/utility_mining_workshop_2018/
Utility-driven mining and learning from data has received emerging attentions from KDD communities due to its high potential in wide applications, covering finance, biomedicine, manufacturing, e-commerce, social media, etc. Current research topics in utility-driven mining focused primarily on discovering patterns of high value (eg, high profit) in large databases, or analyzing/learning the important factors (eg, economic factors) in the 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.
The workshop aims at bringing together academic and industrial researchers and practitioners from data mining, machine learning and other interdisciplinary communities, in the collaborative effort of identifying and discussing major technical challenges, recent results and potential topics on the emerging fields of Utility-Driven Mining. This workshop will focus on real world experiences, inherent challenges, as well as new research methods/applications desired.
The Utility-Driven Mining (UDM) workshop will discuss a broad variety of topics related to utility-driven mining, including:
Theory and core methods for utility mining and learning
Utility patterns mining in large datasets, e.g., high-utility itemset mining, high-utility sequential patterns/rules mining, high-utility episode mining, and other novel patterns
Analysis and learning of novel utility factors in mining and learning process
Predictive modeling/learning, clustering and link analysis that incorporate utility factors
Incremental utility mining and learning
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/learning
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-driven mining
The workshop will be held on August 19th 2018 in London (United Kingdom) at the KDD 2018 conference.
Submissions are limited to 9 pages, and must formatted according to the Standard ACM Conference Proceedings Template.
Papers will be evaluated based on the evaluation criteria of the main KDD 2018 conference for research papers.
In particular, papers must present original research that is not under consideration in other journals, conferences and workshops.
The submission procedure will be announced soon.
Paper submissions due: May 8, 2018
Paper notifications: June 8, 2018
Workshop date: August 19, 2018
For any questions, please contact the organizing committee:
Organizing committee:
Vincent S. Tseng (National Chiao Tung University, Taiwan)
Philip S. Yu (University of Illinois at Chicago, USA)
Jerry Chun-Wei Lin (Harbin Institute of Technology Shenzhen, China)
Philippe Fournier-Viger (Harbin Institute of Technology Shenzhen, China)
Program committee:
Aijun An (York University, USA)
Longbing Cao (University of Technology, Sydney Australia)
Unil Yun (Sejong University, Korea)
Tzung-Pei Hong (National University of Kaohsiung, Taiwan)
Wensheng Gan (Harbin Institute of Technology Shenzhen, China)
Wei Song (North China University of Technology, China)
Chowdhury Farhan Ahmed (University of Dhaka, Dhaka)
Bay Vo (Ho Chi Minh City University of Technology, Vietnam)
Yun Sing Koh (University of Auckland, New Zealand)
Morteza Zihayat (Ryerson University, Canada)
Pinar Karagoz (Middle East Technical University, Turkey)
Srikumar Krishnamoorthy (Indian Institute of Management, Ahmedabad, India)
Jun-Feng Qu (Hubei University of Arts and Science, China)
Uday Kiran (University of Tokyo, Japan)
Youcef Djenouri (Southern Denmark University, Denmark)
Tin Truong Chi (University of Data, Vietnam)
Ke Wang (Simon Fraser University, Canada)
