MAFIA: A Performance Study of Mining Maximal Frequent Itemsets

13 years 6 months ago

Download www.cs.cornell.edu

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,

Real-time Traffic

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

claim paper

» Parallel Mining of Maximal Frequent Itemsets from Databases

» A Treebased Approach for Efficiently Mining Approximate Frequent Itemsets

» Feasible itemset distributions in data mining theory and application

» UFIMT an uncertain frequent itemset mining toolbox

» A Fast Algorithm for Mining ShareFrequent Itemsets

» CFIStream mining closed frequent itemsets in data streams

» Algorithms for Discovery of Frequent Superset Rather than Frequent Subset

» Mining Frequent Itemsets in a Stream

Post Info
More Details (n/a)

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

Comments (0)

Sciweavers

MAFIA: A Performance Study of Mining Maximal Frequent Itemsets

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

Explore & Download

Productivity Tools

Document Tools

Image Tools

Sciweavers