A fast APRIORI implementation

By Ferenc Bodon. Workshop on Frequent Itemset Mining Implementations, 2003

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A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (2009, background): “As the focus of this survey is on the correct evaluation of recommender systems, and not on sophisticated algorithms for computing recommendation lists, we limit ourselves to a set of vry simple collaborative filtering algorithms.”
Frequent pattern mining with uncertain data (2009, result): “Similar to [ 5 ], when we deal with a transaction, we maintain two indices, one is for the transaction, the other points to the node of the trie tree.”
Frequent item set mining (2012, background): “Some additional techniques, as well as other specialized algorithms, can be found, for instance, in Refs 14–15, 49, 50.”
FAST ALGORITHM FOR MINING ASSOCIATION RULES (2014, methodology): “The efficiency of frequent itemset mining algorithms is determined mainly by three factors: the way candidates are generated, the data structure that is used and the implementation details [ 8 ] . In this section we will show these points in our algorithm.”