Abstract

Association discovery is one of the most studied tasks in the field of data mining. However, far more attention has been paid to how to discover associations than to what associations should be discovered. This talk: highlights shortcomings of the dominant frequent pattern paradigm; illustrates benefits of the alternative top-k paradigm; and presents the self-sufficient itemsets approach to identifying potentially interesting associations.

Short Bio

Prof. Geoff Webb has been the director of the Monash University Center for Data Science and IEEE Distinguished Speaker since 2017. He was chief editor of the leading magazine Data Mining and Knowledge Discovery from 2005 to 2014. He has been chairman of the program committee for the two main conferences at data mining, ACM SIGKDD and IEEE ICDM, and also organizer of ICDM. He is a technical advisor to BigML Inc, which incorporated its association discovery software, Magnum Opus, as one of the central components of its Cloud-based Machine Learning service. He developed many of the main mechanisms for discovering support-trust associations in the late 1980s. The OPUS algorithm is a benchmark in the field of research related to rule discovery. He pioneered several areas of research as diverse as black-box user modeling, interactive data analytics, and statistically-sound pattern discovery. He developed many machine learning algorithms that are widely implemented.

About Talks@DCC 

The mission of the Talks@DCC seminars is to bring together researchers and students, to foster discussions and promote scientific awareness and collaboration.

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