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| Latest News
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 [November
9th, 2010]
Workshop program
(DOC)
(PDF) is posted online.
[May
7th, 2010] Webpage Kickoff
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| Workshop Description
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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
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Topics Areas
Top
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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
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| Important Dates
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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.
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| Program Committee Members
(tentative) Top |
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Shingo Aoki, Osaka Prefecture University,
Japan
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Wanpracha Art Chaovalitwongse, Rutgers, the
State University of New Jersey, USA
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Ian Davidson,
University of California, Davis
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Bin Gao,
Microsoft Research Asia
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Heng Huang, University of
Texas at Arlington
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Masato Koda, University of Tsukuba, Japan
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Gang Kou, University of Electronic Science
and Technology of China, China
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Brian Kulis,
University of California at Berkeley
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James Kwok, Hongkong
University of Science and Technology
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Kin Keung Lai, City University of Hong Kong,
Hong Kong, China
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Heeseok Lee, Korea Advanced Institute
Science and Technology, Korea
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Jianping Li, Chinese Academy of Sciences,
China
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Lingfeng Niu, Chinese Academy of Sciences,
China
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David Olson, University of Nebraska at
Lincoln, USA
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Yi Peng, University of Electronic Science
and Technology of China, China
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Jie Tang,
Tsinghua University, China
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Dacheng Tao, Nanyang
Technological University, Singapore
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Fei Sha, University of
Southern California
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Vikas Sindhwani, IBM
T. J. Watson Research
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Masashi Sugiyama,
Tokyo Institute of Technology
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Jimeng
Sun, IBM T. J. Watson Research Lab
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Yangqiu Song, IBM Research
China
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Gang Wang, Microsoft
Research Asia
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John Wang, Montclair State University, USA
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Shouyang Wang, Chinese Academy of Sciences,
China
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Linli Xu, University of
Alberta, Canada
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Shuicheng Yan,
National University of Singapore
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Xiaobo Yang, Daresbury Laboratory,
Warrington, UK
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Kai Zhang, Lawrence
Berkeley National Lab
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Ning Zhong, Maebashi Institute of
Technology, Japan
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Xiaofei Zhou, Chinese Academy of Sciences,
China
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