【主讲】熊辉，美国Rutgers新泽西州立大学副教授

【主题】Efficient Discovery of Confounders in Large Data Sets

【时间】2010-5-6（周四）10:30-12:00

【地点】清华经管学院伟伦楼453室

【语言】英文

【主办】清华大学现代管理研究中心

清华大学经济管理学院管理科学工程系

报告摘要

Given a large transaction database, association analysis is concerned with efficiently finding strongly related objects. Unlike traditional associate analysis, where relationships among variables are searched at a global level, we examine confounding factors at a local level. Indeed, many real-world phenomena are localized to specific regions and times. These relationships may not be visible when the entire data set is analyzed. Specially, confounding effects that change the direction of correlation is the most significant. Along this line, we propose to efficiently find confounding effects attributable to local associations. Specifically, we derive an upper bound by a necessary condition of confounders, which can help us prune the search space and efficiently identify confounders. Experimental results show that the proposed CONFOUND algorithm can effectively identify confounders and the computational performance is an order of magnitude faster than benchmark methods.

个人简历

Dr. Hui Xiong received his Ph.D. from theUniversityofMinnesota. He is currently an Associate Professor atRutgersUniversity, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), an IBM ESA Innovation Award (2008), the Junior Faculty Teaching Excellence Award (2007) and the Junior Faculty Research Award (2008) at theRutgersBusinessSchool. His general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive business applications. He is an Associate Editor of the Knowledge and Information Systems journal. He has served regularly in the organization committees and the program committees of a number of international conferences and workshops. More detailed information is available athttp://datamining.rutgers.edu.