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Lecture
FP-Growth: Itemset Mining
Description
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.
Official source
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