New real world applications of data mining and machine learning have shown that popular methods may appear to be too simple and restrictive. Mining more complex, larger and generally speaking “more difficult” data sets pose new challenges for researchers and ask for novel and more complex approaches. We organize this workshop where we want to promote research and discussion on more complex and advanced methods for the particularly demanding data and web mining problems. Although we welcome submissions concerning methods based on different principles, we would like also to see among them new research on using optimization techniques. The new data and web mining problems are definitely more complex than traditional ones and they could result in more difficult non-convex optimization formulations. We would like to focus interest of data mining community on various challenging issues which come up while using complex methods to deal with the difficult data mining problems.
Suggested topics include (but are not limited to) the following:
·Optimization methods for data or web mining and machine learning
·Multiple criteria perspectives in data mining and learning
·Supporting human evaluation of patterns discovered from data
·Combined classifiers for complex learning problems
·New methods for constructing and evaluating on-line recommendation
·Mining “difficult” data – concerning different aspects of data difficulty (time changing, class imbalanced, partially labeled, multimedia, semi-structured or graph data)
·Mining spatial data and images
·Identifying the most challenging applications and key industry drivers (where both theories and applications point of views have to meet together)
Submission Guidelines:
CMDWM invites original high-quality papers. Each accepted paper will be allocated 4 pages in the proceedings and all papers accepted for workshops will be included in the Workshop Proceedings published by the IEEE Computer Society Press, and will be available at the workshops.
July 22, 2018: Submission of Workshop papers
August 19, 2018: Notification of Workshop paper acceptance
Workshop Oganizers：
Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science
Key Laboratory of Big Data Mining and Knowledge Management and also with Research Center on Fictitious Economy & Data Science
Workshop organizers:
Yong Shi
Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science
E-mail: yshi@ucas.ac.cn
Lingfeng Niu
Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science
E-mail: niulf@ucas.ac.cn
The postal mailing address: Room 215, Buliding 6, No 80, Zhongguancun Donglu,
Haidian District, Beijing, 100190
Name of the corresponding workshop organizer: Lingfeng Niu
Program Committee：
Xiaojun Chen
The Hong Kong Polytechnic University, HK, China
Zhengxin Chen
University of Nebraska at Omaha, USA
Kun Guo
University of the Chinese Academy of Sciences, China
Jing He
Victoria University, Australia
Gang Kou
University of Electronic Science and Technology of China, China
Kin Keung Lai
City University of Hong Kong, Hong Kong, China
Heeseok Lee
Korea Advanced Institute Science and Technology, Korea
Jiming Peng
University of Illinois at Urbana-Champaign, USA
Yi Peng
University of Electronic Science and Technology of China, China
Zhiquan Qi
University of the Chinese Academy of Sciences, China
Yingjie Tian
Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science, China
Bo Wang
University of Internal Business and Economics, China
Jianping Li
Chinese Academy of Sciences, China
Lingling Zhang
University of Chinese Academy of Sciences, China
Yanchun Zhang
Victoria University, Australia
Ning Zhong
Maebashi Institute of Technology, Japan
Xiaofei Zhou
Chinese Academy of Sciences, China
