Presented by Geoff Webb, Monash University, *IEEE Distinguish Speaker*
Association discovery is a fundamental data mining task. The primary statistical approach to association discovery between variables is log-linear analysis. Classical approaches to log-linear analysis do not scale beyond about ten variables. By melding the state-of-the-art in statistics, graphical modeling, and data mining research, we have developed efficient and effective algorithms for log-linear analysis, performing in seconds log-linear analysis of datasets with thousands of variables and providing a powerful statistically-sound method for creating compact models of complex high-dimensional multivariate distributions.
Professor Webb is the Director of the Monash University Centre for Data Science. He was editor-in-chief of the premier data mining journal, Data Mining and Knowledge Discovery from 2005 to 2014. He has been Program Committee Chair of the two top data mining conferences, ACM SIGKDD and IEEE ICDM, as well as General Chair of ICDM. He is the Director of the Monash University Center for Data Science. He is a Technical Advisor to BigML Inc, who have incorporating his best of class association discovery software, Magnum Opus, as a core component of their cloud based Machine Learning service. He developed many of the key mechanisms of support-confidence association discovery in the late 1980s. His OPUS search algorithm remains the state-of-the-art in rule search. He pioneered multiple research areas as diverse as black-box user modeling, interactive data analytics, and statistically-sound pattern discovery. He has developed many best-of-class machine learning algorithms that are widely deployed.