Association Rules: Frequent Itemsets

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.