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