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This document compares the Matrix Apriori and FP-Growth algorithms for mining association rules from transactional databases without candidate generation. It analyzes the performance of the two algorithms in two phases: 1) building the data structure and 2) finding frequent itemsets. The algorithms are tested on two datasets with different characteristics. The findings show that algorithm performance depends on dataset characteristics and minimum support threshold. Matrix Apriori outperforms FP-Growth for t

Author

crana10

Language

EN