Semantic Preserved Generative Adversarial Network-Driven Enhanced Candidate Generation for Efficient and Scalable Frequent Itemset Mining in Large-Scale Data Analytics

Frequent itemset mining is a key task in data mining, particularly for analyzing customer purchase behavior in large-scale transaction datasets. This paper proposes a novel method called SPGAN-ECG-FISM-LSDA—Semantic Preserved Generative Adversarial Network-driven Enhanced Candidate Generation for Efficient and Scalable Frequent Itemset Mining. The approach begins by converting transaction data into bit vectors and generating compact candidate itemsets using subset discovery, thereby reducing mem