Introduction

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:

• 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)
• ostering 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

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
• Learning from label proportions
CFP of OEDM'19, See attachment ‘OEDM 2019 - CFP.pdf’

Important Dates

All deadlines are at 11:59PM Pacific Daylight Time.

Submissions Due Date: August 7, 2019
Notifications of Acceptance: September 4, 2019
Conference dates: November 8 – 11, 2019

Submissions

• Submission Guidelines
• Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format (link), including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review. All submissions will be triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity. The following sections give further information for authors. Please refer to the ICDM regular submission requirment for more information: http://icdm2019.bigke.org/

• How to prepare your submissions
• The authors shall omit their names from the submission. For formatting templates with author and institution information, simply replace all these information in the template by “Anonymous”.

In the submission, the authors’ should refer to their own prior work like the prior work of any other author, and include all relevant citations. This can be done either by referring to their prior work in the third person or referencing papers generically. For example, if your name is Smith and you have worked on clustering, instead of saying “We extend our earlier work on distance-based clustering (Smith 2005),” you might say “We extend Smith’s (Smith 2005) earlier work on distance-based clustering.”

The authors shall exclude citations to their own work which is not fundamental to understanding the paper, including prior versions (e.g., technical reports, unpublished internal documents) of the submitted paper. They should reference only necessary work using point (2). Hence, do not write: “In our previous work [3]” as it reveals that citation 3 is written by the current authors.

The authors shall remove mention of funding sources, personal acknowledgments, and other such auxiliary information that could be related to their identities. These can be reinstituted in the camera-ready copy once the paper is accepted for publication.

The authors shall make statements on well-known or unique systems that identify an author, as vague in respect to identifying the authors as possible.

The submitted files shall be named with care to ensure that authors’ anonymity is not compromised by the file name. For example, do not name your submission “Smith.pdf”, instead give it a name that is descriptive of the title of your paper, such as “ANewApproachtoClustering.pdf” (or a shorter version of the same).

Accepted papers will be published in the conference proceedings by the IEEE Computer SocietyPress.

• Online Submission System
• All manuscripts are submitted as full papers and are reviewed based on their scientific merit. The reviewing process is confidential. There is no separate abstract submission step. There are no separate industrial, application, short paper or poster tracks. Manuscripts must be submitted electronically in online submission system. We do not accept email submissions.

Note that all accepted papers will be included in the IEEE ICDM 2019 Workshops Proceedings volume published by IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library. ​​Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.

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 2018. This workshop (OEDM’19) is a series workshop and continuation of the theme of ICDM 2009-2018 Workshop Optimization Based Techniques for Emerging Data Mining Problems, ICDM 2005-2007 Workshop Optimization-based Data Mining Techniques with Applications.

The Statistics results from 2005-2018 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:

• ODEM’18 http://icdm2018.org/
• ODEM’17 http://icdm2017.bigke.org/
• ODEM’16 http://icdm2016.eurecat.org/workshops-program/
• ODEM’15 http://icdm2015.stonybrook.edu/content/workshops
• OEDM’14 http://icdm2014.sfu.ca/program_workshops.html
• OEDM’13 http://icdm2013.rutgers.edu/workshops
• OEDM’12 http://icdm2012.ua.ac.be/content/workshops
• OEDM’10 http://users.cis.fiu.edu/ ~taoli/icdm10-workshop/
• OEDM’09 http://users.cis.fiu.edu/~taoli/icdm09-workshop/
• OEDM’07 http://dm.ist.unomaha.edu/IEEE-ICDM07-WH-OPT-DM-call-for-papers.htm/
• OEDM’06 http://www.dtke.ac.cn/meeting/meeting_5.htm
• OEDM’05 http://www.cacs.louisiana.edu/~icdm05/.

Organizer

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

• Shingo Aoki, Osaka Prefecture University, Japan
• Wanpracha Art Chaovalitwongse, Rutgers, the State University of New Jersey,  USA
• Ian Davidson, University of California, Davis
• Bin Gao, Microsoft Research Asia
• Guangyan Huang, Victoria Unviersity
• Heng Huang, University of Texas at Arlington
• Masato Koda, University of Tsukuba, Japan
• Gang Kou, University of Electronic Science and Technology of China, China
• Brian Kulis, University of California at Berkeley
• James Kwok, Hongkong University of Science and Technology
• Kin Keung Lai, City University of Hong Kong, Hong Kong, China
• Heeseok Lee, Korea Advanced Institute Science and Technology, Korea
• Jianping Li, Chinese Academy of Sciences, China
• Yingjie Tian, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science
• Lingfeng Niu, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science
• David Olson, University of Nebraska at Lincoln, USA
• Yi Peng, University of Electronic Science and Technology of China, China
• Fei Sha, University of Southern California
• Vikas Sindhwani, IBM T. J. Watson Research Center
• Masashi Sugiyama, Tokyo Institute of Technology
• Jimeng Sun, IBM T. J. Watson Research Center
• Yangqiu Song, Microsoft Research Asia
• Jie Tang, Tsinghua University, China
• Dacheng Tao, University of Technology, Sydney, Australia
• Gang Wang, Tencent Technologies Inc. China
• John Wang, Montclair State University, USA
• Shouyang Wang, Chinese Academy of Sciences, China
• Linli Xu, University of Science and Technology, China
• Shuicheng Yan, National University of Singapore
• Xiaobo Yang, Daresbury Laboratory, Warrington, UK
• Kai Zhang, Simens Corporate Research, Princeton
• Ning Zhong, Maebashi Institute of Technology, Japan
• Xiaofei Zhou, Chinese Academy of Sciences, China

Contact information

General Co-Chairs:

• Prof. Shi Yong, University of Nebraska at Omaha /Chinese Academy of Sciences , Email: yshi@ucas.ac.cn,
Address: Room 203, Building 6, No. 80 Zhongguancun East Road, Haidian District, Beijing P.R.China, 100190.

Program Co-Chairs:

• Prof. Chris Ding, University of Texas at Arlington, Email: chqding@cse.uta.edu,
Address: 500 UTA Blvd, Room 640.
• Prof. Yingjie Tian Chinese Academy of Sciences, Email: tyj@ucas.ac.cn,
Address: Room 205, Building 6, No. 80 Zhongguancun East Road, Haidian District, Beijing P.R.China, 100190.
• Prof. Zhiquan Qi Chinese Academy of Sciences, Email: qizhiquan@ucas.ac.cn,
Address: Room 215, Building 6, No. 80 Zhongguancun East Road, Haidian District, Beijing P.R.China, 100190.
• Dr. Fan Meng Central University of Finance and Economics, Email: mengfan@cufe.edu.cn,
Address: Room 336, Building 4, Shahe Higher Education Park, Changping District，Beijing, P.R.China, 102206

Schedule

Our workshop is scheduled to hold on the morning of Nov 8 2019. Due to room limitations of the conference, OEDM'19 and DMIIOT'19 (Workshop on Data Mining in Industrial Internet of Things) have merged to together and named "OEDM & DMIIOT". DMIIOT is an industry oriented workshop focusing on the combination of data mining techniques and internet of things, and the detailed information can be found in its website. The accepted papers of both workshops will be presented alternately and we believe this will bring new thoughts to attendees from both workshops.

Workshop time:

08:00-11:50, Nov 8 2019 08:00-11:50

Workshop Room:

TBD

Presentation schedule:

There are 5 accepted papers from OEDM'19 and DMIIOT'19 together, and the detailed schedule is as follows.

• 08:00-08:40
Paper ID: S10201
Authors: Yuhan Lin, Minglong Lei, and Lingfeng Niu
Title: Optimization Strategies in Quantized Neural Networks: A Review

• 08:40-09:20
Paper ID: S15201
Authors: Tomonari Masada, Takumi Eguchi, and Daisuke Hamaguchi
Title: Difference between Similars: a Novel Method to Use Topic Models for Sensor Data Analysis

• 09:20-10:00
Paper ID: S10203
Authors: Kazuki Koyama, Keisuke Kiritoshi, and Tomonori Izumitani
Title: Discovering Sparse and Ununiform Lag Structure Using VAR Models with Latent Group LASSO

• 09:20-10:00: Coffee Break

• 10:30-11:10
Paper ID: S15202
Authors: Devon Peticolas, Russell Kirmayer, and Deepak S. Turaga
Title: M'ımir: Building and Deploying an ML Framework for Industrial IoT

• 11:10-11:50
Paper ID: S10205
Authors: Xing Nie, Yang Hu, Guoliang Ma, and Fanhua Shang
Title: RASVRG: Robust Accelerated Stochastic Variance Reduction Gradient for Sparse Subspace Clustering