Association Rule Mining

This lecture covers the concept of association rule mining, focusing on finding rules of the form Body → Head with support and confidence measures. It explains the scoring function, support, and confidence calculations, as well as the Apriori algorithm for frequent itemset discovery. The instructor discusses the problem of association rule mining, the two-step approach involved, and the importance of exploiting the Apriori property. Additionally, alternative measures of interest in association rules, handling quantitative attributes, and improving Apriori for large datasets are explored. The lecture concludes with the FP-growth algorithm, which discovers frequent itemsets without candidate generation.