Apr. 2, 2001 to Apr. 6, 2001
Doug Burdick , Cornell University
Manuel Calimlim , Cornell University
Johannes Gehrke , Cornell University
Abstract: We present a new algorithm for mining maximal frequent itemsets from a transactional database. Our algorithm is especially efficient when the itemsets in the database are very long. The search strategy of our algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms. Our implementation of the search strategy combines a vertical bitmap representation of the database with an efficient relative bitmap compression schema. In thorough experimental analysis of our algorithm on real data, we isolate the effect of the individual components of the algorithm. Our performance numbers show that our algorithm outperforms previous work by a factor of three to five.
Doug Burdick, Manuel Calimlim, Johannes Gehrke, "MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases", ICDE, 2001, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2001, pp. 0443, doi:10.1109/ICDE.2001.914857