MAFIA: A Performance Study of Mining Maximal Frequent Itemsets

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.

• Type: Conference
• Year: 2003
• Where: FIMI
• Authors: Douglas Burdick, Manuel Calimlim, Jason Flannick, Johannes Gehrke, Tomi Yiu
• Keywords: Data Mining, FIMI 2003, MAFIA Algorithm, Maximal Frequent Itemsets, Space Pruning Techniques