简介：吴月华，加拿大约克大学（York University）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。吴教授研究领域包括金融统计、空间统计、高维数据统计、变点检验以及在环境科学等交叉学科。目前当选国际统计学会的会员（Elected member of International Statistical Institute），承担多项加拿大政府重要科研项目，发表学术论文百余篇，其中包括5篇国际最顶级期刊Proceedings of the National Academy of Sciences of the United States of America（PNAS，美国国家科学院院刊）论文。
报告题目：Association rule mining and market basket analysis
教授观点：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 first briefly review the Apriori algorithm, and then 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 some data examples.