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吴月华教授学术报告

  发布日期:2017-05-08  浏览量:374


报告题目: Gibbs-sampling-induced stochastic search procedure for association rule mining

: 吴月华(加拿大约克大学教授、国际统计学会的会员

报告时间: 201759(周二)  15:30-16:30

报告地点: 磬苑校区澳门赌搏网站大全H306

报告摘要:Current algorithms for association rule mining from transaction data are mostly deterministic and enumerative. They can be computationally intractable even for mining a dataset containing just a few hundred transaction items, if no action is taken to constrain the search space. In this talk, we introduce a Gibbs-sampling-induced stochastic search procedure to randomly sample association rules from the itemset space, and perform rule mining from the reduced transaction dataset generated by the sample. A general rule importance measure is also proposed to direct the stochastic search so that, as a result of the randomly generated association rules constituting an ergodic Markov chain, the overall most important rules in the itemset space can be uncovered from the reduced dataset with probability 1 in the limit.  We end the talk by presenting simulation studies and real data examples.

欢迎各位老师、同学届时前往!

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专家概况: 吴月华,加拿大约克大学(York University)数学与统计系教授;1989年获得美国匹兹堡大学统计学博士学位,师从世界著名统计学家C.R. Rao。她的主要研究方向包括空间统计、M-估计、模型选择、变点检测、非参数估计、金融统计等, 以及在环境科学、信息科学、计量经济学、生物医学等领域中的应用。目前当选国际统计学会的会员(Elected member of International Statistical Institute),承担了多项加拿大重要科研项目,发表学术论文100篇,包括4篇论文发表在国际最顶级期刊Proceedings of the National Academy of Sciences of the United States of America(美国国家科学院院刊)上。

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