------------------------------------------------------------------------- Title: Association Pattern Discovery: A Statistical Computing Perspective Abstract The problem of association pattern mining is to develop techniques for finding groups of highly-correlated objects from massive data. This problem is important for various application domains, such as bioinformatics, market basket study, and medical data analysis. A large body of association mining work was motivated by the difficulty of efficiently identifying highly correlated objects using traditional statistical correlation measures. This has led to the use of alternative interest measures, such as support and confidence, despite the lack of a precise relationship between these new interest measures and statistical correlation measures. However, this approach tends to generate too many spurious patterns involving objects which are poorly correlated. In this talk, I provide a precise relationship between Pearson's correlation coefficient and the support measure. I also present an efficient algorithm called TAPER to identify highly-correlated pairs of objects by contributing two algorithmic ideas: the monotonic upper bound of Pearson's correlation coefficient and novel pruning of candidates based on the ordering of object-pairs containing a common object. Experimental results from real-world data sets show that the TAPER algorithm can be an order of magnitude faster than brute-force alternatives. Short Biography: Hui Xiong is an assistant professor in the Management Science and Information Systems department at Rutgers, the State University of New Jersey. His research interests include data mining, spatial databases, statistical computing, and Geographic Information Systems (GIS). He has published over 20 technical papers in peer-reviewed journals and conference proceedings and is the co-Editor-in-Chief of the book entitled Encyclopedia of Geographical Information Science. He has also served on the program committees for a number of conferences and workshops. Dr. Xiong received a Ph.D. degree in Computer Science from the University of Minnesota. More details are available at http://cimic.rutgers.edu/~hui. -------------------------------------------------------------------------------------