2010 Workshop on Optimization Based Methods for Emerging Data Mining Problems  

(OEDM'10)

Held in conjunction with

 

Venue: UTS Haymarket Campus, Haymarket, Building 5, Level 1, 5B.1.12

Date:  December 14, 2010

 


Scope and Program
 
Latest News
 
Workshop Program (DOC) (PDF)

Workshop Description

Topics of Interest

Important Dates



Paper Submissions

Submission


Organization

Organizers

Program Committee


Relevant Links
 
OEDM'09
 
DMKD Special Issue on Data Mining using Matrices and Graphs
 
DMMT'08 (with SIGKDD 2008)
 
DMMT'09 (with SIGKDD 2009)
 
 ICDM 2010
 
SCIS at FIU

 



Latest News    

 

[November 9th, 2010]  Workshop program  (DOC) (PDF) is posted online.

         [May 7th, 2010]  Webpage Kickoff

 

Workshop Description    

 

This workshop (OEDM’10) is a continuation of the theme of ICDM 2009 workshop on Optimization Based Methods for Emerging Data Mining Problems (OEDM’09). OEDM’09 is the first workshop on optimization-based methods for emerging data mining problems held annually with the ICDM Conference.  The workshop builds on the success of previous workshop and provides a unique platform for researchers and practitioners working on data mining using optimization based techniques to share and disseminate recent research results.   

Classical optimization techniques have found widespread use in solving various data mining problems, among which convex optimization has occupied the center-stage because of its 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.

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.

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

Topics Areas    Top

 

Topic areas for the workshop include (but are not limited to) the following:

Methods and algorithms:

  • Principal Component Analysis and Singular value decomposition for clustering and dimension reduction
  • Graph-based learning (classification, semi-supervised learning and unsupervised learning)
  • Graph/Hypergraph based methods
  • Matrix/Tensor based methods
  • Kernel/graph kernel/structured kernel learning
  • Large margin methods
  • Large scale numerical optimization
  • Randomized algorithms
  • Sparse algorithms, compressive sensing
  • Regularization techniques
  • Stochastic optimization
  • Theoretical advances

Application areas

  • Collaborative filtering
  • Genomics and Bioinformatics by fusing different information sources
  • Information search and extraction from Web using different domain knowledge
  • Scientific computing and computational sciences
  • Sensor network
  • Social information retrieval by fusing different information sources
  • Social Networks analysis
  • Text processing and information retrieval
  • Image processing and analysis
  • Scientific computing and computational sciences

 

Important Dates    Top

  •  July 23rd, 2010   August 9th, 2010:   Electronic submission of full papers
  •  September 20th, 2010:  Author notification
  •  October 11th, 2010:  Submission of Camera-ready papers
  •  December 14th, 2010: Workshop in Sydney, Australia.

Paper Submissions    Top

The electronic submission web site for research papers is available at: ICDM Workshop Submission Site

Papers should be at most 10 pages long, single-spaced, in IEEE conference format, in font size 10 or larger with 1-inch margins on all sides.


Workshop Organizers    Top
 

Workshop General Co-Chairs:

Program Co-Chairs:

Co-Chairs

Affiliation

Address

Email

Phone

Dr. Fei Wang

IBM T.J. Watson Research Center

 19 Skyline Drive
 Hawthorne, NY 10532

fwang@us.ibm.com

+1 607-255-2461

Dr. Jing He

Victoria University

Centre for Applied Informatics, PO Box 14428, Melbourne City MC, VIC 8001 Australia

jing.he@vu.edu.au

+61 3 9919 4676

Local Chair:

Dr. Jing He, Victoria University

Note: for inquiries please send e-mail to taoli AT cs.fiu.edu.

Program Committee Members (tentative)   Top

  • 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

  • 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

  • Lingfeng Niu, Chinese Academy of Sciences, China

  • David Olson, University of Nebraska at Lincoln, USA

  • Yi Peng, University of Electronic Science and Technology of China, China

  • Jie Tang, Tsinghua University, China

  • Dacheng Tao, Nanyang Technological University, Singapore

  • Fei Sha, University of Southern California

  • Vikas Sindhwani, IBM T. J. Watson Research

  • Masashi Sugiyama, Tokyo Institute of Technology

  • Jimeng Sun, IBM T. J. Watson Research Lab

  • Yangqiu Song, IBM Research China

  • Gang Wang, Microsoft Research Asia

  • John Wang, Montclair State University, USA

  • Shouyang Wang, Chinese Academy of Sciences, China

  • Linli Xu, University of Alberta, Canada

  • Shuicheng Yan, National University of Singapore

  • Xiaobo Yang, Daresbury Laboratory, Warrington, UK

  • Kai Zhang, Lawrence Berkeley National Lab

  • Ning Zhong, Maebashi Institute of Technology, Japan

  • Xiaofei Zhou, Chinese Academy of Sciences, China