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.

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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