The 2013 IEEE/WIC/ACM International Conference on Web Intelligence (WI13) and the 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT-13) will be held in Atlanta, USA, Nov. 17-20, 2013. The two co-located conferences are sponsored by IEEE Computer Society Technical Committee on Intelligent Informatics (TCII), Web Intelligence Consortium (WIC), and ACM-SIGART. During the conference, Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science will hold a workshop focus on Optimization-based Data Mining and Web Intelligence on Nov. 17 2013. 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 and web intelligence problems.