Lecture

Association Rules: Frequent Itemsets

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In course

CS-423: Distributed information systems

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. ...

Description

This lecture covers the concept of Association Rule Mining, focusing on Frequent Itemsets and Alternative Measures of Interest. It explains the process of transforming quantitative attributes, improving Apriori for large datasets, and the importance of partitioning. The instructor discusses the FP-Growth algorithm, FP-Tree data structure, and the steps involved in FP-Tree construction and frequent itemset extraction. The lecture concludes with a performance comparison between FP-Growth and Apriori algorithms, highlighting the advantages and disadvantages of each approach.

Instructor

Karl Aberer

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.

Official source

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Association Rule Mining