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 (MCQP) 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:


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:

Please find more detailed information in the CFP ‘OEDM 2018 - CFP’


As OEDM has been lasting for over 10 years and attracted a great number of high quality papers, we will invite the former attendees to present their lastest researches in our workshop.

We have also organized several international conferences including International Conference on Information Technology and Quantitative Management(ITQM 2013-2017), International Conference on Data Science(ICDS 2014-2017). We will invite the former attendees to contribute their papers to our workshop.

Past Records

The Workshop on Optimization Based Techniques for Emerging Data Mining Problems has gathered the key organizers of the previous ICDM workshops on Optimization Based Techniques for data mining problems from 2005 to 2017. This workshop (OEDM’18) is a series workshop and continuation of the theme of ICDM 2009-2017 Workshop Optimization Based Techniques for Emerging Data Mining Problems, ICDM 2005-2007 Workshop Optimization-based Data Mining Techniques with Applications.

 The Statistics results from 2005-2017 shown that our workshop was a success and more than 100 people attended the workshop. The workshop builds on the success of previous workshops and provides a unique platform for researchers and practitioners working on data mining using optimization based techniques to share and disseminate recent research results. Information about previous workshops can be found:


Yong Shi received the Ph.D. degree in management science and computer system from The University of Kansas, Lawrence, KS, USA. He is currently a Professor with the Chinese Academy of Sciences, Beijing, China, where he serves as the Director of Research Center on Fictitious Economy and Data Science. He is also a Professor and a Distinguished Chair of Information Technology with the College of Information Science and Technology, University of Nebraska Omaha, Omaha, USA. His research interests include data mining, information overload, optimal system designs, multiple-criteria decision making, decision support systems, and information and telecommunications management. Dr. Shi is the Editor-in-Chief of International Journal of Information Technology and Decision Making and Annals of Data Science.

Chris Ding is professor in University of Texas at Arlington. His research areas are machine learning / data mining, bioinformatics, information retrieval, web link analysis, and high performance computing. His research are supported by National Science Foundation grants and University of Texas Regents STARS Award. Professor Ding has published about 200 papers that were cited over 25500 times (google scholar).

Yingjie Tian is the Professor of Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences. He received his first degree in Mathematics (1994), Masters in Applied Mathematics (1997), Ph.D. in Management Science and Engineering. His research interests include support vector machines, optimization theory and applications, data mining.

Zhiquan Qi received a Master degree and Ph.D. degree in College of Science from China Agricultural University in 2006 and 2011. Currently he is a associate professor of research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences. His research interests include data mining, and the application in weak label learning.

Fan Meng received his Bachelor degree in Department of Information Management and Information System of Peking University in 2012, and Ph.D. degree Management Science and Engeering from University of Chinese Academy of Sciences. He is current a lecturer Central University in Finance and Ecnomics. His research interests include data mining, weak label learning and its applications in computer vision and business intellegence.

Tentative program committee

Contact information

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