题目: Causal analysis for association discovery
Association analysis is an important technology in data mining, and has been widely used in many application areas. However, associations in data can be spurious and they do not indicate causal-effect relationships that are ultimate goals for many scientific explorations and social studies. While the techniques for association discovery become mature, the problem for identifying non-spurious associations becomes prominent. In this talk, I will discuss two methods for testing causal effect relationships rooted in statistical analysis and the use of them for filtering non-causal associations in association discovery. The first method uses the Mantel and Haenszel test for partial association, and the second method uses a cohort study approach. I will also discuss the implementation issues in large data sets for combining relationship exploration and evaluation.
Dr Jiuyong Li is a Professor and an associate Head of School at the School of Information Technology and Mathematical Sciences of University of South Australia. He leads the Data Analytics Group in the School. His main research interests are in data mining, bioinformatics, and data privacy. He has led multiple Australian Research Council Discovery projects. He has published more than 100 papers, mostly in leading journals and conferences in the areas. His software tools have been used in several real world projects. He has been chair (and PC chair) of Australasian Data Mining Conference and Australasian Joint Conference on Artificial Intelligence. He has received senior visiting fellowships from Nokia Foundation, the Australian Academy of Science, and Japan Society of Promotion of Science.