OEDM 2011 - The 6th Workshop on Optimization Based Techniques for Emerging Data Mining Problems

Website icdm2011.cs.ualberta.ca | Edit Freely

Category OEDM 2011

Deadline: July 23, 2011 | Date: December 10, 2011

Venue/Country: Vancouver, Canada

Updated: 2011-05-29 20:01:34 (GMT+9)

Call For Papers - CFP

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 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

Topics of Interests

The topics of interests of this workshop are (but not limited to) the following:

Method and Algorithms

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

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