2009年8月25日-27日最优化技术和应用-第五届网络计算与管理大会专题

● 会议名称（中文）： 最优化技术和应用-第五届网络计算与管理大会专题

● 会议名称（英文）： Optimization-based Data Mining Techniques and Applications - Special Session of NCM2009 5th International Joint Conference on INC, IMS and IDC 

● 所属学科： 信息科学与系统科学　 管理学　 数学　 计算机科学技术　 
 
● 会议类型： 国际会议 

● 会议论文集是否检索： ISTP EI
 
● 开始日期： 2009-8-25

● 结束日期： 2009-8-27

● 所在国家： 韩国 

● 所在城市： 国外  
 
● 具体地点： 汉城

● 主办单位： 韩国Sogang大学

● 协办单位： IEEE

● 承办单位： 电子科技大学

● 议题：

optimization
data mining
business intelligence

● 会议主席： 石勇、彭怡、寇纲

● 全文截稿日期： 2009-5-10

● 论文录用通知日期 2009-5-30

● 会务组联系方式

联系人： 寇纲
联系电话： 15982498501
E-mail： kougang@yahoo.com
 
● 会议注册费： 约500美元

● 会议网站： http://www.aicit.org/ncm/inv_sessions.html#is4

● 会议背景介绍：

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 organizers and their colleagues extended such a research idea into classification via multiple criteria linear programming (MCLP) and multiple criteria quadratic programming (MCQP), which differs from statistics, decision tree induction, and neural networks. So far, there are hundreds of scholars around the world have been actively working on the field of using optimization techniques to handle data mining problems. This workshop will call for papers to the researchers in the above interface fields for their participation in the conference. In addition to attract the technical papers, this workshop will particularly encourage the submissions of optimization-based data mining applications, such as customer segmentation, credit assessment management, fraud detection, information intrusion, bio-informatics, etc. 

● 征文范围及要求：

Many data mining tasks, such as classification, prediction, clustering, and model selection, can be formulated as optimization problems. Optimization techniques have great potentials in data mining and business intelligence. However, most current research of optimization-based data mining focus on model development and algorithm design, other steps in the Knowledge Discovery Process did not get enough attention. This workshop intends to promote the research interests in the connection of optimization and data mining in the whole KDD process, including data cleaning, data preparation, data transformation, model construction, results evaluation and knowledge presentation, as well as real-life applications among the growing data mining communities.

Intended audience:
Anyone who is interested in optimization, data mining, business intelligence, related areas and real-life applications.