This lecture covers Association Rule Mining, focusing on finding rules like Body → Head with support and confidence measures, illustrated with a shopping basket analysis example. It explains single- and multi-dimensional rules, scoring functions, the Apriori algorithm, and the steps involved in mining association rules.
Karl Aberer received his PhD in mathematics in 1991 from the ETH Zürich. From 1991 to 1992 he was postdoctoral fellow at the International Computer Science Institute (ICSI) at the University of California, Berkeley. In 1992, he joined the Integrated Publication and Information Systems institute (IPSI) of GMD in Germany, where he was leading the research division Open Adaptive Information Management Systems. In 2000 he joined EPFL as full professor. Since 2005 he is the director of the Swiss National Research Center for Mobile Information and Communication Systems (
NCCR-MICS, www.mics.ch
). He is member of the editorial boards of VLDB Journal, ACM Transaction on Autonomous and Adaptive Systems and World Wide Web Journal. He has been consulting for the Swiss government in research and science policy as a member of the Swiss Research and Technology Council (
SWTR
) from 2003 - 2011.
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This course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems.
Explores Association Rule Mining, emphasizing Frequent Itemsets and Alternative Measures of Interest, including the FP-Growth algorithm and performance comparison.
