【call for paper】Optimization Based Techniques for Emerging Data Mining - Workshop of OEDM 2017

  • 李秀明
  • 发布于 2017-04-10
  • 262

 Dear  colleagues, we  are going to propose the following workshop at ICDM2017 Conference.

 

                                                                       Paper Submission Deadline: August 7, 2017

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                                                         Optimization Based Techniques for Emerging Data Mining

                                                                                  - Workshop of OEDM 2017

                                                                        New Orleans, USA,  November 18, 2017

                                                                                   (http://icdm2017.bigke.org/)

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1.     Scope of the workshop:

Using optimization techniques to deal with data separation and data analysis goes back to more than thirty years ago. According to O. L. Mangasarian, his group has formulated linear programming as a large margin classifier in 1960’s. Nowadays classical optimization techniques have found widespread use in solving various data mining problems, among which convex optimization and mathematical programming have occupied the center-stage. With the advantage of convex optimization’s elegant property of global optimum, many problems can be cast into the convex optimization framework, such as Support Vector Machines, graph-based manifold learning, and clustering, which can usually be solved by convex Quadratic Programming, Semi-Definite Programming or Eigenvalue Decomposition. Another research emphasis is applying mathematical programming into the classification. For the last twenty years, the researchers have extensively applied quadratic programming into classification, known as V. Vapnik’s Support Vector Machine, as well as various applications.

As time goes by, new problems emerge constantly in data mining community, such as Time-Evolving Data Mining, On-Line Data Mining, Relational Data Mining and Transferred Data Mining.  Some of these recently emerged problems are more complex than traditional ones and are usually formulated as nonconvex problems. Therefore some general optimization methods, such as gradient descents, coordinate descents, convex relaxation, have come back to the stage and become more and more popular in recent years. From another side of mathematical programming, In 1970’s, A. Charnes and W.W. Cooper initiated Data Envelopment Analysis where a fractional programming is used to evaluate decision making units, which is economic representative data in a given training dataset. From 1980’s to 1990’s, F. Glover proposed a number of linear programming models to solve discriminant problems with a small sample size of data.  Then, since 1998, multiple criteria linear programming (MCLP) and multiple criteria quadratic programming (MQLP) has also extended in classification. All of these methods differ from statistics, decision tree induction, and neural networks. So far, there are more than 200 scholars around the world have been actively working on the field of using optimization techniques to handle data mining problems.

This workshop will present recent advances in optimization techniques for, especially new emerging, data mining problems, as well as the real-life applications among. One main goal of the workshop is to bring together the leading researchers who work on state-of-the-art algorithms on optimization based methods for modern data analysis, and also the practitioners who seek for novel applications. In summary, this workshop will strive to emphasize the following aspects:

Presenting recent advances in algorithms and methods using optimization techniques

Addressing the fundamental challenges in data mining using optimization techniques

Identifying killer applications and key industry drivers (where theories and applications meet)

Fostering interactions among researchers (from different backgrounds) sharing the same interest to promote cross-fertilization of ideas.

Exploring benchmark data for better evaluation of the techniques

3. Topic areas

This workshop intends to promote the research interests in the connection of optimization and data mining as well as real-life applications among the growing data mining communities. It calls for papers to the researchers in the above interface fields for their participation in the conference. The workshop welcomes both high-quality academic (theoretical or empirical) and practical papers in the broad ranges of optimization and data mining related topics including, but not limited to the following:

        Convex optimization for data mining problems

        Multiple criteria and constraint programming for data mining problems

        Nonconvex optimization (Gradient Descents, DC Programming…)

        Linear and Nonlinear Programming based methods

        Matrix/Tensor based methods (PCA, SVD, NMF, Parafac, Tucker…)

        Large margin methods (SVM, Maximum Margin Clustering…)

        Randomized algorithms (Random Projection, Random Sampling…)

        Sparse algorithms (Lasso, Elastic Net, Structural Sparsity…)

        Regularization techniques (L2 norm, Lp,q norm, Nuclear Norm…)

        Combinatorial optimization

        Large scale numerical optimization

        Stochastic optimization

        Graph analysis

        Theoretical advances

Application areas

In addition to attract the technical papers, this workshop will particularly encourage the submissions of optimization-based data mining applications, such as credit assessment management, information intrusion, bio-informatics, etc. as follows:

        Association rules by optimization

        Artificial intelligence and optimization

        Bio-informatics and optimization

        Cluster analysis by optimization

        Collaborative filtering

        Credit scoring and data mining

        Data mining and financial applications

        Data warehouse and optimization

        Decision support systems

        Genomics and Bioinformatics by fusing different information sources

        Healthcare and Biomedical Informatics

        Image processing and analysis

        Information overload and optimization

        Information retrieval by optimization

        Intelligent data analysis via optimization

        Information search and extraction from Web using different domain knowledge

        Knowledge representation models

        Multiple criteria decision making in data mining

        Optimization and classification

        Optimization and economic forecasting

        Optimization and information intrusion

        Scientific computing and computational sciences

        Sensor network

        Social information retrieval by fusing different information sources

        Social Networks analysis

        Text processing and information retrieval

        Visualization and optimization

        Web search and decision making

        Web mining and optimization

        Website design and development

        Wireless technology and performance

4.       Workshop Organizers:

General Co-Chairs:

Prof. Shi Yong, University of Nebraska at Omaha /Chinese Academy of Sciences , email: yshi@ucas.ac.cn,

mail: Room 203, Building 6, No. 80 Zhongguancun East Road, Haidian District, Beijing 100190.

Program Co-Chairs:

Prof. Chris Ding, University of Texas at Arlington, email: chqding@cse.uta.edu, mail: 500 UTA Blvd, Room 640.

Prof. Yingjie Tian Chinese Academy of Sciences, email: tyj@ucas.ac.cn, mail: Room 215, Building 6, No. 80 Zhongguancun East Road, Haidian District, Beijing 100190.

Dr. Zhiquan Qi  Chinese Academy of Sciences, email: qizhiquan@ucas.ac.cn, mail: Room 215, Building 6, No. 80 Zhongguancun East Road, Haidian District, Beijing 100190.

Mr. Fan Meng University of Chinese Acedemy of Sciences, email: im_feixie@163.com, mail: Room 210, Building 6, No. 80 Zhongguancun East Road, Haidian District, Beijing 100190.

5.       Important date:

All deadlines are at 11:59PM Pacific Daylight Time.

  • Full paper submission: August 7, 2017
  • Author notification: September 7, 2017
  • Camera-ready submission: September 15, 2017
  • Conference: November 18, 2017

6.       Submission

  • http://icdm2017.bigke.org/

7.       Tentative PC.

  • Shingo Aoki, Osaka Prefecture University, Japan
  • Wanpracha Art Chaovalitwongse, Rutgers, the State University of New Jersey,  USA
  • Ian Davidson, University of California, Davis
  • Bin Gao, Microsoft Research Asia
  • Guangyan Huang, Victoria Unviersity
  • Heng Huang, University of Texas at Arlington
  • Masato Koda, University of Tsukuba, Japan
  • Gang Kou, University of Electronic Science and Technology of China, China
  • Brian Kulis, University of California at Berkeley
  • James Kwok, Hongkong University of Science and Technology
  • Kin Keung Lai, City University of Hong Kong, Hong Kong, China
  • Heeseok Lee, Korea Advanced Institute Science and Technology, Korea
  • Jianping Li, Chinese Academy of Sciences, China
  • Yingjie Tian,Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science
  • Lingfeng Niu, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science
  • David Olson, University of Nebraska at Lincoln, USA
  • Yi Peng, University of Electronic Science and Technology of China, China
  • Fei Sha, University of Southern California
  • Vikas Sindhwani, IBM T. J. Watson Research Center
  • Masashi Sugiyama, Tokyo Institute of Technology
  • Jimeng Sun, IBM T. J. Watson Research Center
  • Yangqiu Song, Microsoft Research Asia
  • Jie Tang, Tsinghua University, China
  • Dacheng Tao, University of Technology, Sydney, Australia
  • Gang Wang, Tencent Technologies Inc. China
  • John Wang, Montclair State University, USA
  • Shouyang Wang, Chinese Academy of Sciences, China
  • Linli Xu, University of Science and Technology, China
  • Shuicheng Yan, National University of Singapore
  • Xiaobo Yang, Daresbury Laboratory, Warrington, UK
  • Kai Zhang, Simens Corporate Research, Princeton
  • Ning Zhong, Maebashi Institute of Technology, Japan
  • Xiaofei Zhou, Chinese Academy of Sciences, China