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Mining Dependent Patterns
时间:2017-05-21 点击:
报告人:Prof. Hansheng Lei (雷寒生 教授)
邀请人:苗启广
报告时间:2017年5月23日下午16:00
报告地点:主楼四区107会议室
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Hansheng Lei received his B.S. from Ocean University of China in 1998 and M.S. from University of Science and Technology of China in 2001, both in computer science. He finished his Ph.D. study in the Department of Computer Science and Engineering, University at Buffalo, the State University of New York in December 2005. He joined the Computer and Information Sciences Department at the University of Texas at Brownsville (UTB) as an assistant professor in Jan 2006. He is promoted to Associate Professor with tenure fall 2012. His current research interests lie in distributed machine learning, computer vision and data mining in a High Performance Computing (HPC) environment. He has published over thirty articles in peer-reviewed conferences/journals. He has completed several projects funded by NSF and US Department of Education with a total amount over $3.5M. He has served as a proposal reviewer for a variety of funding agencies, including NSF, Department of Education and Department of Commerce. He is currently co-organizing the 2018 1st International Conference on Data Intelligence and Security, to be held on April 8-10, 2018 at South Padre Island, USA.
 
Abstract:
Association Rule (AR) mining has been studied intensively for the past two decades. Essentially, AR models the conditional probabilities of itemsets. However, AR mining generates an overwhelming number of rules which limits its capability in mining real nuggets. We re-examined the problem and propose to start mining on dependent relationships instead of conditional relationships. In contrast to AR mining, dependence mining has received much less attention in the literature. Here a new model, Dependent Pattern (DP) mining is presented. DP has a solid base in classical statistics and at the same time is suitable for large scale computation with the property of downward closure. We validate the model from different perspectives using a variety of datasets. Experimental results demonstrate that DP has remarkable advantages over AR mining and other related methods. This work serves as a proof of concept. Future work will focus on the theoretical analysis of DP’s scalability.