【会议征文】Call for papers for Workshop of OEDM2013(5.10)

  • 发布于 2013-05-10
  • 4403

Dear  colleagues, we  are going to propose the following workshop at OEDM2013 Conference
Paper Submission Deadline: August 3, 2013

Optimization Based Techniques for Emerging Data Mining

- Workshop of OEDM2013

Dallas, Texas,  December 8-11, 2013

(http://icdm2013.rutgers.edu/call-for-workshops)

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

 

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

Paper Submission

Paper submissions should be limited to a maximum of 8 pages (only one additional page is allowed and extra payment is required for the additional page). The papers must be in English and should be formatted according to the IEEE 2-column format (see the Author Guidelines at http://www.computer.org/portal/web/cscps/formatting ). The workshop only accepts on-line submissions. Please use the Workshop Submission Page on the OEDM2013  website to submit your paper. The authors of accepted contributions will be asked to submit final version and register for the conference.

All papers accepted for workshops will be included in the Workshop Proceedings published by the IEEE Computer Society Press that are indexed by EI, and will be available at the workshops. Detailed information is available at the conference homepage (http://icdm2013.rutgers.edu/ ).

Important Date:

August 3, 2013: Due date for full workshop papers

 September 24, 2013: Notification of paper acceptance to authors

October 15, 2013: Camera-ready deadline for accepted papers

December 8, 2013: Workshop date

Workshop Co-Chairs:

Yong Shi

College of Information Science and Technology, University of Nebraska at Omaha, NE 68182, USA

E-mail: yshi@unomaha.edu

and

Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science

Beijing 100190, China

E-mail: yshi@ucas.ac.cn

Chris Ding,

University of Texas at Arlington

E-mail: chqding@uta.edu

Yingjie Tian

Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science

E-mail: tyj@ucas.ac.cn

Zhiquan Qi

Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science

E-mail: qizhiquan@ucas.ac.cn

Program Committee:

Shingo Aoki

Osaka Prefecture University, Japan

Wanpracha Art Chaovalitwongse

Rutgers, the State University of New Jersey, USA

Masato Koda

University of Tsukuba, Japan

Gang Kou

University of Electronic Science and Technology of China, China

Kin Keung Lai

City University of Hong Kong, Hong Kong, China

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OEDM CFP 2013.doc