The 1st ICDM Workshop on Dynamic Feature Mining (DFM’19)
Inconjunction with the IEEE International Conference on Data Mining (IEEE ICDM 2019), Beijing, China, November 8-11, 2019

Description of the workshop

The emerging of various data collecting ways lead to increasing data sets with dynamic features in all areas of science, engineering and businesses. These include sensor data in monitoring systems, trading data in E-business systems and voting data in recommendation systems among many others. For a number of reasons, classical data analysis methods inadequate, questionable, or inefficient when faced with dynamic feature data analyses:

  • The evolving of dynamic feature violates the traditional independent and identically distributed assumption in the areas of data mining and machine learning.

  • The evolving of features may lead to storage problem if the algorithms are batch-model and all the data are utilized during training.

  • The accumulation of features will lead to high dimensional data, which will cause curse of dimensionality reduction problem.

  • The accumulation of features will also lead to computational issues, especially when the frequency of feature changes is high.

Topics

This workshop aims to promote new advances and research directions to address the challenging problems arisen from the evolving nature of features in data mining. Topics of interest include all aspects of dynamic feature mining, including but notlimited to:

  • Systematic researches of how the evolving nature of features affects data mining methods.

  • New mining algorithms for dynamic features in supervised, semi-supervised or unsupervised way.

  • Dimensionality reduction approaches for accumulated of various types of dynamic features.

  • Storage or computation scalable approaches for evolving features.

  • Theoretical underpinning of mining historical features.

  • Datamining applications to real problems in science, engineering or businesses where the features are dynamic.

  • Other learning paradigms arisen from the dynamic nature of features.

Paper submission

Submission Site

High quality original submissions are welcomed for oral and poster presentation at the workshop. The page limit of workshop papers is 4-8 pages in the standard IEEE 2-column format(https://www.ieee.org/conferences/publishing/templates.html), including the bibliography and any possible appendices. Reviewing is blind. Therefore, please do not include author identifying information. All papers must be formatted according to the IEEE Computer Society proceedings manuscript style, following IEEE ICDM 2019 submission guidelines, which are the same as for the main conference (except the page limit). All accepted workshop papers will be published in the IEEE Computer Society Digital Library (CSDL) and IEEE Xplore, and indexed by EI.

Important dates

  • Abstract deadline: 11st August, 2019.

  • Full Paper deadline: 18th August, 2019.

  • Workshop paper notifications: September 4, 2019

  • Camera-ready deadline for the final version of accepted papers: September 8, 2019

Registration& Expenses

Every workshop paper must have at least one full paid conference registration in order to be published. Check the main conference pages for details.

Schedule

We have updated the sechdule on 5th, Nov. Please refer this one.

Schedule of ICDM’19Workshop on DFM

2019.11.8@China National Convention Center (CNCC), Room 302A

Time

Content

Speaker

13:30-13:40

Welcome and opening remarks

13:40-14:15

Invited talk: Active Feature Acquisition with Lower Cost

Sheng-Jun Huang, Nanjing University of Aeronautics and  Astronautics

14:15-14:25

Oral 1: Dynamic Spatio-Temporal Feature Learning via Graph Convolution in 3D  Convolutional Networks

Jun Li, Xianglong Liu, Jun Xiao, Hainan Li,  Shuo Wang, and Liang Liu

14:25-14:35

Oral 2: Consensus Graph Weighting via Trace  Ratio Criterion for Multi-view Unsupervised Feature Selection

Liang Du, Xiao Lin, and Peng Zhou

14:35-14:45

Oral 3: Capped  l2,1-Norm Regularized Dictionary Coding for Scalable Semi-supervised Learning

Jiao Liu, and Mingbo  Zhao

14:45-14:55

Oral 4: Learning  Effective Representations from Sparse Multimodal Data on Content Curation  Social Networks

Lifang Wu, Bowen Yang,  Meng Jian, Xiuzhen Zhang, and Heng Zhang

14:55-15:05

Oral 5: Prototype Propagation Clustering Based on Large  Margin

Feijiang Li, Yuhua Qian, and Jieting Wang

15:05-15:15

Oral 6: Efficient  Attribute Reduction with Submodular Function Optimization

Xuxia Zhang, Xiaodong  Yue, and Zhikang Xu

15:15-15:25

Oral 7: Few-Shot  Learning based on Attention Relation Compare Network

Xianqin Ma, Chongchong  Yu, Xin Yang, and Xiuxin Chen

15:25-15:50

Coffee break

15:50-16:25

Invited talk: Multi-modal Heterogeneous  Data Representation Learning

Liping Jing, Beijing Jiaotong University

16:25-16:35

Oral 8: A New Multi-view Multi-label Learning with  Incomplete Views and Labels

Changming Zhu, Duoqian  Miao, Rigui Zhou, and Lai Wei

16:35-16:45

Oral 9: A New Classification Method of  Signature Network Node Based on Potential Space Projection

Wei-Jin Jiang, Yang Wang,  Xiao-Liang Liu, and Jia-Hui Chen

16:45-16:55

Oral 10: Mining logic patterns from visual data

Qian Guo, Yu-hua Qian,  and Xinyan Liang

16:55-17:05

Oral 11: Cross-Domain  Deep Collaborative Filtering for Recommendation

Yachen Kang, Feng Zhao,  Sibo Gai, Donglin Wang, and Yi Luo

17:05-17:15

Oral 12: Attribute reduction based on multi-objective  decomposition-ensemble optimizer with rough set and entropy

Jie Yang and Simon  Fong

17:15-17:30

Closing remarks

Program Committee

No.

Name

Organization

1

Cheng Deng

Xidian University  

2

Liang Du

Shanxi University

3

Chen Gong

Nanjing University of Science and Technology

4

Jie Gui

Chinese Academy of Sciences

5

Bo-Jian Hou

Nanjing University

6

Kai Hu

Xiangtan University

7

Zhihui Lai

Shenzhen University

8

Shao-Yuan  Li

Nanjing University of Aeronautics and Astronautics

9

Tongliang  Liu

The University of Sydney

10

Mingsheng  Long

Tsinghua University

11

Tingjin Luo

National University of Defense Technology

12

Yong Luo

Nanyang Technological University

13

Hong Tao

National University of Defense Technology

14

Ruiping  Wang

Chinese Academy of Sciences

15

Erkun Yang

University of North Carolina at Chapel Hill

16

Yun-Hao  Yuan

Yangzhou University

17

Xiaodong  Yue

Shanghai University

18

Changqing Zhang

Tianjin University

19

Zhao Zhang

Hefei University of Technology

20

Mingbo Zhao

Donghua University

21

Sihang Zhou

National  University of Defense Technology

22

Wenzhang  Zhuge

National University of Defense Technology


Workshop organisation

Chenping Hou, hcpnudt@hotmail.com, National University of Defense Technology, China

Min-Ling Zhang, zhangml@seu.edu.cn, Southeast University, China

Cheng Deng, Chdeng.xd@gmail.com, Xidian University, China

Yuhua Qian, jinchengqyh@126.com, Shanxi University, China

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