Efficiently mining maximal frequent itemsets

By Karam Gouda, Mohammed J. Zaki. Proceedings 2001 IEEE International Conference on Data Mining, 2001

499 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b803cf73131354e5e2ce2309317c5a867fe0c634

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Citations in the corpus

Fast vertical mining using diffsets (2003, methodology): “For example, the average diffset size falls below 1 for the last few lengths (over the length interval [11-16] for chess, [9-12] for connect, [7-17] for mushroom, [8-15] for pumsb*, 8 for pumsb, [9-11] for T10, [12-14] for T20).”
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].”
Efficient algorithms for mining closed itemsets and their lattice structure (2005, methodology): “Therehave been several efficient algorithms for mining maximal frequent itemsets, such as MaxMiner [4], Depth-Project [2], Mafia [6], and GenMax [10].”
MAFIA: a maximal frequent itemset algorithm (2005, methodology): “More details on the LMFI concept can be found in the paper by Gouda and Zaki [35] .”
Efficiently Using Prefix-trees in Mining Frequent Itemsets (2003, methodology): “GenMax, also proposed by Gouda and Zaki [4], takes an approach calledprogressive focusing to do maximality testing.”
Fast algorithms for frequent itemset mining using FP-trees (2005, background): “Since MaxMiner only looks for the maximal FIs, the search space can be reduced.”
Association mining (2006, methodology): “…Dunham 1998 AS-CPA sampling Naive extension Max-eclat Zaki et al. 2000 Eclat MFS Max-clique Zaki et al. 2000 Clique MFS MAFIA Burdick et al. 2001 Depth Project MFS Accrual optimisation GenMax Gouda & Zaki 2001 Depth Project MFS Progressive focusing & diffsets FAST Chen et al. 2002 …”
CHARM: An Efficient Algorithm for Closed Itemset Mining (2002, methodology): “The first one is to mine only the maximal frequent itemsets [2, 4, 6, 10, 12], which are typically orders of magnitude fewer than all frequent patterns.”
Moment: maintaining closed frequent itemsets over a stream sliding window (2004, background): “, [4], [11]), only maximalfrequent itemsets are discovered.”
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].”