MAFIA: A Performance Study of Mining Maximal Frequent Itemsets

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We present a performance study of the MAFIA algorithm for mining maximal frequent itemsets from a transactional database. In a thorough experimental analysis, we isolate the effects of individual components of MAFIA, including search space pruning techniques and adaptive compression. We also compare our performance with previous work by running tests on very different types of datasets. Our experiments show that MAFIA performs best when mining long itemsets and outperforms other algorithms on dense data by a factor of three to thirty.

Douglas Burdick, Manuel Calimlim, Jason Flannick,

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Data Mining | FIMI 2003 | MAFIA Algorithm | Maximal Frequent Itemsets | Space Pruning Techniques |

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Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2003
Where	FIMI
Authors	Douglas Burdick, Manuel Calimlim, Jason Flannick, Johannes Gehrke, Tomi Yiu

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MAFIA: A Performance Study of Mining Maximal Frequent Itemsets

Data Mining | FIMI 2003 | MAFIA Algorithm | Maximal Frequent Itemsets | Space Pruning Techniques |

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