Article

MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases

Authors:

Douglas Burdick,

Manuel Calimlim,

Johannes GehrkeAuthors Info & Claims

Proceedings of the 17th International Conference on Data Engineering

Pages 443 - 452

Published: 02 April 2001 Publication History

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Belaid MBessiere CLazaar N(2019)Constraint programming for mining borders of frequent itemsetsProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367032.3367184(1064-1070)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367032.3367184
Pérez-Suárez AMartínez-Trinidad JCarrasco-Ochoa J(2019)A review of conceptual clustering algorithmsArtificial Intelligence Review10.1007/s10462-018-9627-152:2(1267-1296)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10462-018-9627-1
Abuzaid FKraft PSuri SGan EXu EShenoy AAnanthanarayan ASheu JMeijer EWu XNaughton JBailis PZaharia M(2018)DIFFProceedings of the VLDB Endowment10.14778/3297753.329776112:4(419-432)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.14778/3297753.3297761
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MAFIA: A Maximal Frequent Itemset Algorithm

We present a new algorithm for mining maximal frequent itemsets from a transactional database. The search strategy of the algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms that significantly improve ...
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
ICDE '01: Proceedings of the 17th International Conference on Data Engineering

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 ...
Efficient algorithms for mining high-utility itemsets in uncertain databases

High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in transaction databases, which considers both quantity and profit of items. However, most algorithms for mining high-utility itemsets (HUIs) assume that the ...

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Published In

Proceedings of the 17th International Conference on Data Engineering

April 2001

610 pages

ISBN:0769510019

Publisher

IEEE Computer Society

United States

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Published: 02 April 2001

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Belaid MBessiere CLazaar N(2019)Constraint programming for mining borders of frequent itemsetsProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367032.3367184(1064-1070)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367032.3367184
Pérez-Suárez AMartínez-Trinidad JCarrasco-Ochoa J(2019)A review of conceptual clustering algorithmsArtificial Intelligence Review10.1007/s10462-018-9627-152:2(1267-1296)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10462-018-9627-1
Abuzaid FKraft PSuri SGan EXu EShenoy AAnanthanarayan ASheu JMeijer EWu XNaughton JBailis PZaharia M(2018)DIFFProceedings of the VLDB Endowment10.14778/3297753.329776112:4(419-432)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.14778/3297753.3297761
Fasihy HShahraki M(2018)Incremental mining maximal frequent patterns from univariate uncertain dataKnowledge-Based Systems10.1016/j.knosys.2018.04.001152:C(40-50)Online publication date: 15-Jul-2018
https://dl.acm.org/doi/10.1016/j.knosys.2018.04.001
Ai DPan HLi XGao YHe D(2018)Association rule mining algorithms on high-dimensional datasetsArtificial Life and Robotics10.1007/s10015-018-0437-y23:3(420-427)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10015-018-0437-y
Lin JZhang JFournier-Viger PHong TZhang J(2017)A two-phase approach to mine short-period high-utility itemsets in transactional databasesAdvanced Engineering Informatics10.1016/j.aei.2017.04.00733:C(29-43)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.aei.2017.04.007
Tran ATruong TLe B(2017)Efficiently mining association rules based on maximum single constraintsVietnam Journal of Computer Science10.1007/s40595-017-0096-24:4(261-277)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s40595-017-0096-2
Benhamou B(2017)Local and global symmetry breaking in itemset miningAnnals of Mathematics and Artificial Intelligence10.1007/s10472-016-9528-480:1(91-112)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s10472-016-9528-4
Tin THai DNgan H(2016)Structure of frequent itemsets with extended double constraintsVietnam Journal of Computer Science10.1007/s40595-015-0056-73:2(119-135)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1007/s40595-015-0056-7
Durand NQuafafou M(2016)Frequent Itemset Border Approximation by DualizationTransactions on Large-Scale Data- and Knowledge-Centered Systems XXVI - Volume 967010.1007/978-3-662-49784-5_2(32-60)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1007/978-3-662-49784-5_2
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MAFIA: A Maximal Frequent Itemset Algorithm

MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases

Efficient algorithms for mining high-utility itemsets in uncertain databases

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