Fast vertical mining using diffsets

By Mohammed J. Zaki, Karam Gouda. 2003

728 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

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Citations in the corpus

MurTree: Optimal Decision Trees via Dynamic Programming and Search (2020, methodology): “Such a strategy resembles techniques used in the frequent itemset community (Zaki and Gouda (2003)).”
Frequent Subtree Mining – An Overview (2004, neutral): “Another frequency counting method is used in vertical mining algorithms [49], which associates an occurrence list (or tid list ) with each candidate itemset, i.e., a list of tids of transactions that support it.”
An efficient algorithm for discovering frequent subgraphs (2004, methodology): “This is because the transaction framework that is assumed by these algorithms cannot be used to effectively model the data sets in these domains.”
Survey on Frequent Pattern Mining (2003, methodology): “Recently, Zaki and Gouda [42, 43] proposed a new approach to efficiently compute the support of an itemset using the vertical database layout.”
Frequent subgraph discovery (2001, methodology): “Eﬃcient algorithms for ﬁnding frequent itemsets—both sequential and non-sequential—in very large transaction databases have been one of the key success stories of data mining research [2, 1, 26, 12, 3, 24].”
Graph indexing: a frequent structure-based approach (2004, result): “fx ‰ fi. Such a solution generalizes a similar idea which was presented in [ 22 ], but handles multiple rather than one id list.”
Frequent substructure-based approaches for classifying chemical compounds (2003, methodology): “For every candidate, FSG takes the intersection of TID-lists of its parents, and performs the subgraph isomorphism only on the transactions contained in the resulting TID-list.”
Fast algorithms for frequent itemset mining using FP-trees (2005, background): “To solve this problem, another type of a frequent itemset, called closed frequent itemset (CFI), was proposed in [24].”
Moment: maintaining closed frequent itemsets over a stream sliding window (2004, background): “In the second solution (e.g., [14], [ 15 ]), only closed frequent itemsets are discovered.”
Finding Frequent Patterns in a Large Sparse Graph* (2004, background): “Both horizontal and vertical approaches have been previously used to find frequent subgraphs in the graphtransaction setting [33, 44, 68, 8] and have their origins on algorithms developed for finding frequent itemsets and sequences [2, 3, 26, 72].”
CHARM: An Efficient Algorithm for Closed Itemset Mining (2002, methodology): “The diffset format of CHARM is resilient to sparsity (as shown in [20]) and it continues to outperform other methods.”
Efficient algorithms for mining closed itemsets and their lattice structure (2005, methodology): “We showed in [23] that diffsets drastically cut down (by orders of magnitude) the size of memory required to store intermediate results.”
Review and Comparison of Associative Classification Data Mining Approaches (2014, neutral): “In 2003, Zaki and Gouda [24] proposed a variation of the Eclat algorithm, called dEclat.”
Efficiently Using Prefix-trees in Mining Frequent Itemsets (2003, methodology): “Subsequently, it was shown in [10], that the algorithm dEclat outperforms FPgrowth on most datasets.”
Frequent sub-structure-based approaches for classifying chemical compounds (2005, methodology): “Finally, for determining the actual frequency of each subgraph, FSG reduces the number of subgraph isomorphism operations by using TID-lists [29], [74], [ 83 ], [84] to keep track of the set of transactions that supported the frequent patterns discovered at the previous level of the lattice.”
Efficiently mining maximal frequent itemsets (2001, methodology): “In an extensive study [10], we showed that diffsets are very short compared to their tidsets counterparts, and are highly effective in improving the running time of vertical methods.”
A review of associative classification mining (2007, methodology): “Finally, AC algorithms (Thabtah et al., 2004, 2005) extend tid-lists intersections methods of vertical association rule data layout (Zaki et al., 1997; Zaki & Gouda, 2003) to solve classification benchmark problems.”