Association Rules: Mining and Analysis

This lecture covers the concept of association rules in data mining, focusing on measures like confidence and lift, as well as techniques such as transaction reduction, sampling, and partitioning for efficient rule mining. The instructor explains the FP-growth algorithm and the construction of FP-trees, emphasizing the divide and conquer strategy for frequent itemset extraction. The lecture also delves into the derivation of conditional FP-trees and the performance comparison between FP-growth and Apriori algorithms.