MAFIA: a maximal frequent itemset algorithm for transactional databases

By Douglas Burdick, Manuel Calimlim, Johannes Gehrke. Proceedings / International Conference on Data Engineering, 2001

835 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/06942d8f2afe265379a2f9bf432a9c6e58cf9e84

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Citations in the corpus

CHARM: An Efficient Algorithm for Closed Itemset Mining (2002, methodology): “We compare CHARM against previous methods for mining closed sets such as Close [14], Closet [15], Mafia [6] and Pascal [3].”
Efficiently mining frequent trees in a forest (2002, background): “0, [0, 6] 0, [1,5] 0, [3, 3] 0, [4, 4] 0, [5, 5] 0, [6, 6] 0, [2, 3]”
Efficiently Using Prefix-trees in Mining Frequent Itemsets (2003, methodology): “MAFIA is introduced in [3] by Burdicket al.”
MAFIA: a maximal frequent itemset algorithm (2005, neutral): “An earlier version of this paper first appeared in the Proceedins of the International Conmference on Data Engineering 2001 [33].”
SPIN: mining maximal frequent subgraphs from graph databases (2004, methodology): “Our current work benefits extensively from existing algorithms for maximal itemset mining such as [3, 7] and frequent subtree mining algorithms [1, 21].”
Sequential PAttern mining using a bitmap representation (2002, methodology): “A similar approach has been used for the problem of mining frequent itemsets in MaxMiner [3] and MAFIA [5].”
CLOSET+: searching for the best strategies for mining frequent closed itemsets (2003, methodology): “, [14, 5, 18]) clearly elaborate that the depth-first search methods are usually more efficient than the breadth-first search methods like A-close.”
Clustering algorithms: A comparative approach (2016, methodology): “In [47], two subspace clustering methods were compared: MAFIA (Adaptive Grids for Clustering Massive Data Sets) [48] and FINDIT (A Fast and Intelligent Subspace Clustering Algorithm Using Dimension Voting) [49].”
Efficiently mining maximal frequent itemsets (2001, methodology): “Mafia [4] is the most recent method for mining the MFI.”
Efficient algorithms for mining closed itemsets and their lattice structure (2005, methodology): “The first one is to mine only the maximal frequent itemsets [2], [4], [6], [10], [13], which are typically orders of magnitude fewer than all frequent patterns.”
Fast vertical mining using diffsets (2003, methodology): “MAFIA [6] and SPAM [3] use vertical bit-vectors for fast itemset and sequence mining respectively.”
DATA MINING TECHNIQUES (2003, methodology): “Another promising direction was the development of methods to mine maximal [9, 15, 32, 49] and closed itemsets [59, 61, 83].”
Frequent pattern mining: current status and future directions (2007, methodology): “Another efﬁcient method MAFIA , proposed by Burdick et al. (2001), uses vertical bitmaps to compress the transaction id list, thus improving the counting efﬁciency.”