FP-Growth: Itemset Mining

This lecture covers the FP-Growth algorithm for mining frequent itemsets in transaction datasets. It explains the FP-Tree construction, frequent itemset extraction, divide and conquer strategy, conditional FP-Tree derivation, and performance comparison with Apriori. The instructor discusses the advantages of FP-Growth, such as dataset compression and efficiency, along with its limitations for high support thresholds and memory requirements.