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Optimization-based Data Mining Techniques with Applications
- Workshop of IEEE ICDM¡¯07
The Seventh IEEE International Conference on Data Mining Sponsored by the IEEE Computer Society
Scope of the workshop:
For last ten years, the researchers have extensively applied quadratic programming into classification, known as V. Vapnik¡¯s Support Vector Machine, as well as various applications. However, 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. 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, the organizer and his colleagues extended such a research idea into classification via multiple criteria linear programming (MCLP) and multiple criteria quadratic programming (MQLP), which differs from statistics, decision tree induction, and neural networks. So far, there are more than 100 scholars around the world have been actively working on the field of using optimization techniques to handle data mining problems. 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:
Association rules by Optimization Artificial Intelligence and Optimization Bio-informatics and Optimization Cluster Analysis by Optimization Credit Scoring and Data Mining Web Mining and Optimization Data Mining and Financial Applications Data Warehouse and Optimization Decision Support Systems Information Overload and Optimization Information Retrieval by Optimization Intelligent Data Analysis via Optimization Knowledge Representation Models Multiple Criteria Decision Making in Data Mining Optimization and Classification Optimization and Economic Forecasting Optimization and Information Intrusion Visualization and Optimization Web Search and Decision Making Website Design and Development Wireless Technology and Performance
Submission Methods and Publication The limitation of each submission is a maximum of 6 pages, including of all references figures, and tables. All papers should be submitted in IEEE proceedings format (two columns). Please use IEEE Proceedings guideline in preparing the submission. It can be found at: http://www.computer.org/portal/pages/cscps/cps/final/icdm06.xml The submissions will be handled electronically. It should be
submitted to The workshop proceedings will be published by the IEEE Digital Library and distributed during the workshop, October 28, 2007.
Important Date: Submission Due Date: July 1, 2007 Accepted Date: August 1, 2007 Camera-ready Due Date: August 17, 2007 Workshop
Date:
October 28,
Organizer:
Yong Shi College
of Information Science and Technology, E-mail: yshi@unomaha.edu
E-mail: yshi@gucas.ac.cn
Members of the Program Committee:
Shingo Aoki
Wanpracha Art Chaovalitwongse Rutgers,
the State
Masato Koda
Kin Keung Lai City
Heeseok Lee
David Olson
Jiming Peng University of Illinois at Urbana-Champaign,
John Wang
Shouyang Wang Chinese
Xiaobo Yang Daresbury
Laboratory,
Ning Zhong Maebashi
Institute of
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