题目：Efficient Discovery of Confounders in Large Data Sets 报告人：Hui Xiong
Associate Professor
Rutgers University 时间：6月26日10:30-11:30
地点：蒙民伟楼404会议室 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. 简历：
Dr. Hui Xiong received his Ph.D. from the University of Minnesota and the B.E
degree from the University of Science and Technology of China (USTC). He is
currently an Associate Professor at Rutgers University, 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 the Rutgers Business School.
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 at http://datamining.rutgers.edu.
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