题目：Boosting Association Rule Mining in Large Datasets via Gibbs Sampling
嘉宾：吴月华教授（加拿大约克大学)
时间：2016年5月10日下午4:00-5:00
地点：管理科研大楼1008教室

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. In the simulation study and a real genomic data example, we show how to boost association rule mining by an integrated use of the stochastic search and the Apriori algorithm.

Joint work with Qian, Rao, and Sun

报告人简介：吴月华是加拿大约克大学数学和统计系教授，她的主要研究方向：空间统计，M-估计，模型选择，变点检测，非参数估计等, 以及在环境科学、信息科学、计量经济学中的应用。承担了多项加拿大重要科研项目，已发表学术论文100多篇,包括3篇论文发表在国际最顶级期刊PNAS（美国国家科学院院刊）。

统计与金融系

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