Exploratory Pattern Discovery

Many data mining techniques seek a single model that optimises some criterion with respect to available sample data. In contrast, exploratory pattern discovery finds all patterns that satisfy user specified constraints with respect to the sample data. This approach has many desirable properties which has seen it widely applied. This tutorial covers key techniques including association rules, frequent itemsets, k-optimal rule discovery, top-k itemsets, sequential pattern discovery, and contrast discovery.

Bio

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.