The extraction of patterns displaying significant association with a target feature is a key data mining task with wide application in many domains, including social networks analysis and cancer genomics. The identification of such patterns from modern datasets poses several computational and statistical challenges, due to the massive size of modern datasets and to the exponential number of patterns to be considered. In this talk I will present our recent work that addresses some of these challenges. First, I will present an algorithm to mine the top-k significant patterns while rigorously controlling the family-wise error rate of the output. Our algorithm enables the extraction of the most significant patterns from large datasets that could not be analyzed by the state-of-the-art. Second, I will present our work on extracting high-quality approximations of the most significant patterns using a random sample of a transactional dataset, according to various interestingness measures, while providing rigorous guarantees on the quality of the approximation. Our algorithm vastly s peeds up the discovery of subgroups with respect to analyzing the whole dataset.
Fabio Vandin is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy. His research interests are in efficient and rigorous algorithms for the extraction of useful information from large amounts of data. He has applied his methods mostly to problems in computational biology and biomedicine, but his work has found application in a variety of areas, including social network analysis and wireless networks. He received his PhD in Information Engineering from the University of Padova (Italy), and he has held research positions with various titles at the Department of Computer Science at Brown University (USA) and at the Department of Mathematics and Computer Science at the University of Southern Denmark. In 2016 he has been a Research Fellow at the Simons Institute for the Theory of Computing at the University of California, Berkeley (USA).