K-optimal pattern discovery: An efficient and effective approach to exploratory data mining

Professor Geoff Webb

Research Chair, Faculty of Information Technology, Monash University

Date and time: 11.30am - 12.30pm, Friday 2nd June, 2006

Venue: 10.08.04 (Building 10, Level 8, Room 4)

Abstract:

Most data-mining techniques seek a single model that optimizes an objective function with respect to the data. In most real-world applications several models will equally optimize this function. However, they may not all equally satisfy a user's preferences which will be affected by background knowledge and pragmatic considerations that are infeasible to quantify into an objective function. Thus, the program may make arbitrary and potentially suboptimal decisions. In contrast, methods for exploratory pattern discovery, of which association rule discovery is the best known example, seek all models that satisfy user-defined criteria. This allows the user to select between these models, rather than relinquishing control to the program. K-optimal pattern discovery is an exploratory technique that finds the k patterns that optimize a user-selected objective function while respecting other user-specified constraints. I discuss some important Features that distinguish this approach from the minimum-support technique that underlies association-rule discovery and I describe efficient algorithms that support it.

About the speaker:

Geoff Webb holds a research chair in the Faculty of Information Technology at Monash University. Prior to Monash he held appointments at Griffith University and then Deakin University where he received a personal chair. His primary research areas are machine learning, data mining, and user modelling. He is widely known for his contribution to the debate about the application of Occam's razor in machine learning and for the development of numerous algorithms and techniques for machine learning, data mining and user modelling. His commercial data mining software, Magnum Opus, is marketed internationally by Rulequest Research. He is editor-in-chief of the highest impact data mining journal, Data Mining and Knowledge Discovery and a member of the editorial boards of Machine Learning, ACM Transactions on Knowledge Discovery in Data, and User Modeling and User-Adapted Interaction.

Seminar Organisation

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