ODM 2014 - International Workshop on Optimization-based Data Mining

Prof. Yingjie Tian, tyj-AT-ucas.ac.cn (Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, Beijing, China)
Prof. Zhiquan Qi, qizhiquan-AT-ucas.ac.cn (Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, Beijing, China)
Prof. Yong Shi, yshi-AT-unomaha.edu (College of Information Science and Technology, University of Nebraska at Omaha, USA and Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, Beijing, China)
Prof. Boris Mirkin, bmirkin-AT-hse.ru (HSE, Moscow, Russia)
The fields of data mining and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most data mining approaches. For last several 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 many 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 and web intelligence problems. This workshop intends to promote the research interests in the connection of optimization, data mining and web intelligence as well as real-life applications.