The Schedule of OEDM

  • 发布于 2013-11-06
  • 12555
Dear  colleagues, we  are going to propose the following workshop at OEDM2013
Paper Submission Deadline: August 3, 2013
Optimization Based Techniques for Emerging Data Mining 
- Workshop of OEDM2013
Dallas, Texas,  December 8-11, 2013


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 

Program for 7th December, 2013

08:30-10:00  The report of Oral papers

1. Guo Jing, Zhang Peng, and Zhou Chuan, Information-based Top-k Influential User Discovery in Social Networks
2. Xiaoguang Wang, Xuan Liu, Nathalie Japkowicz, and Stan Matwin, The Class Imbalance Problem in Multi-instance Learning
3. Zhiquan Qi, Yingjie Tian, Xiaodan Yu, Yong Shi, How to Improve the Quality of Pedestrian Detection Using the Priori Knowledge
4. Jiguang Liang, Xiaofei Zhou, Ying Sha, Ping Liu, and Li Guo, Unsupervised Clustering Strategy Based on Label Propagation

10:00-10:30  Coffee Break

10:30-11:30  Invited Report
Sparse Learning for Big Data, Jieping Ye, Arizona State University
Abstract: Sparse methods have been applied extensively in many data mining and machine learning applications, including feature selection, dimensionality reduction, multi-task learning, network construction, and matrix completion. In this talk, we consider sparse methods for (1) variable selection where the  structure over the features can be represented as an undirected graph or a hierarchical tree or a  collection of disjoint groups, (2) multi-source data fusion, and (3) network construction. We will  review state-of-the-art methods for solving these sparse formulations. Finally, We will present novel 
screening strategies which we recently developed to scale sparse methods to large-size problems.  

Bio: Jieping Ye is an Associate Professor of Computer Science and Engineering at the Arizona State University. He is a core faculty member of the Bio-design Institute at ASU. He received his Ph.D. degree in Computer Science from University of Minnesota, Twin Cities in 2005. His research interests  include machine learning, data mining, and biomedical informatics. He has served as Senior Program  Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS, KDD, 
IJCAI, ICDM, SDM, ACML, and PAKDD. He serves as an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. He won the SCI Young Investigator of the Year Award at ASU in 2007, the SCI Researcher of the Year Award at ASU in 2009, and the NSF CAREER Award in 2010. His papers have been selected for the outstanding student paper at ICML in 2004, the KDD best research paper honorable mention in 2010, the KDD best research paper nomination in 2011 and 2012, the SDM best research paper runner up in 2013, and the KDD best research paper runner up in 2013.

11:30-12:00 The report of Oral papers

5. Jeremias Berg and Matti Jarvisalo,  Optimal Correlation Clustering via MaxSAT 

12:00-13:30 Lunch Break

13:30-14:30  Invited Report
Incremental Optimization of Performance Measures,  Zhi-Hua Zhou,  Nanjing University
Bio: Zhi-Hua Zhou is a Cheung Kong professor at Nanjing University. His research interests mainly include machine learning, data mining, pattern recognition and multimedia information retrieval. He has published more than 100 papers, authored the book “Ensemble Methods: Foundations and Algorithms” (2012), and holds 12 patents. According to GoogleScholar, his publications have received more than 11,000 citations, with an h-index 54. He is the recipient of the IEEE CIS Outstanding Early Career Award, Fok Ying Tung Young Professorship Award, Microsoft Young Professorship Award, and various awards including nine international journal/conference paper or competition awards. He serves/ed as Executive Editor-in-Chief of "Frontiers of Computer Science", Associate Editor-in-Chief of "Chinese Science Bulletin", Associate Editor or Editorial Boards member of "ACM TIST", "IEEE TKDE" and many other journals. He is the Founder of ACML, and Steering Committee member of PAKDD and PRICAI. He served as Area Chair or PC member for almost all top conferences in his areas. He is the Chair of the AI&PR Technical Committee of the China Computer Federation, Chair of the Machine Learning Technical Committee of the China Association of AI, Vice Chair of the Data Mining Technical Committee of the IEEE Computational Intelligence Society, and Chair of the IEEE Computer Society Nanjing Chapter. He is a Fellow of the IAPR and Fellow of the IEEE.

14:30-15:00 The report of Oral papers

15:00-15:30 Tea time

15:30-18:30  The report of Oral papers

6. Guibiao Xu, Cost-Free Learning for Support Vector Machines with a Reject Option”

7. Alnur Ali and Kevyn Collins-Thompson, Robust Cost-Sensitive Confidence-Weighted Classification

8. Zejun Gan, Chaofeng Sha, and Junyu Niu, ”Fast Spectral Clustering with Landmark-based Subspace Iteration”

9. Christos Zigkolis, Savvas Karagiannidis, and Athena Vakali, ”Dissimilarity Features in Recommender Systems
Workshop Co-Chairs:
Yong Shi College of Information Science and Technology, University of Nebraska at Omaha, NE 68182, USA E-mail:
Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science
Beijing 100190, China E-mail: 

Chris Ding, 
University of Texas at Arlington

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

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