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        【call for paper】The 7th Workshop on Complex Methods for Data and Web Mining(CMDWM)

        • 發布于 2020-05-13
        • 21113

        The 7th Workshop on Complex Methods for Data and Web Mining

        The 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '20)

        14-17 December 2020, Melbourne, Australia

        Call For Paper

        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.

        Submission deadline: 1st July, 2020

        Acceptance deadline: 20th September, 2020

        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

        Yang Xiao

        University of Chinese Academy of Sciences, China

        Pei Quan

        University of Chinese Academy of Sciences, China

        Yi Qu

        University of Chinese Academy of Sciences, China

        Minglong Lei

        Beijing University of Technology, Beijing, China.

         

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