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:
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:
All dates are 11:59pm Pacific Daylight Time (PDT).
Submissions Due Date: September 3, 2021
Notifications of Acceptance: September 24, 2021
Camera-Ready Deadline: October 1, 2021
Conference dates: December 7 – 10, 2021
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: https://icdm2021.auckland.ac.nz/cfp/
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 2021 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.
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 2020. This workshop (OEDM’21) is a series workshop and continuation of the theme of ICDM 2009-2020 Workshop Optimization Based Techniques for Emerging Data Mining Problems, ICDM 2005-2007 Workshop Optimization-based Data Mining Techniques with Applications.
The Statistics results from 2005-2020 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:
Prof. Shi Yong , University of Nebraska at Omaha /Chinese Academy of Sciences , Email: email@example.com.
Prof. Chris Ding, University of Texas at Arlington, Email: firstname.lastname@example.org.
Prof. Yingjie Tian , Univeristy of Chinese Academy of Sciences, Email: email@example.com.
Associate Prof. Zhiquan Qi , Univeristy of Chinese Academy of Sciences, Email: firstname.lastname@example.org.
Assistant Prof. Fan Meng, Peking University, Email: email@example.com.
Tentative program committee