New Algorithms for Fast Discovery of Association Rules

By Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, Wei Li. 1997

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Citations in the corpus

Learning Optimized Or's of And's (2015, methodology): “There are many frequently used pattern mining methods such as FP-growth (Han, Pei, and Yin, 2000), Apriori (Agrawal, Srikant, and others, 1994), Eclat (Zaki et al., 1997), etc.”
Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, background): “, all rules that satisfy a minimum support and minimum confidence threshold are found (Agrawal et al. 1995; Zaki et al. 1997; Han et al. 2004), and subsequently filtered and/or ordered according to heuristics.”
Review and Comparison of Associative Classification Data Mining Approaches (2014, unknown): “Therefore, it reduces the need computational time [23].”
Foundations and Advances in Data Mining (2005, background): “A Bayesian interpretation The learning algorithm Equation 4 has an interesting Bayesian interpretation [52, 53]: the data term – that is the first term with the quadratic loss function – is a model of (Gaussian, additive) noise and the RKHS norm (the stabilizer) corresponds to a prior probability on”
A Tree Projection Algorithm for Generation of Frequent Item Sets (2001, background): “A number of algorithms for frequent itemset generation have been proposed [1, 2, 4, 6, 11, 7, 12, 13, 16, 17].”
A new approach to classification based on association rule mining (2006, methodology): “The first part is to compare GARC with C4.5-type [30], CBA [21], NN [10], and SVM [37] classifiers on accuracy.”
SPADE: An Efficient Algorithm for Mining Frequent Sequences (2004, methodology): “Our new algorithm is based on the fast association mining techniques we presented in Zaki et al. (1997).”
Mining molecular fragments: finding relevant substructures of molecules (2002, methodology): “The algorithm maintains parallel embeddings of a fragment into all molecules throughout the growth process and exploits a local order of the atoms and bonds of a fragment to prune the search tree, which results in faster search and allows for a restricted depth first search algorithm, similar to the”
Survey on Frequent Pattern Mining (2003, methodology): “The first algorithm proposed to generate all frequent itemsets in a depth-first manner is the Eclat algorithm by Zaki [41, 44].”
Correlated itemset mining in ROC space: a constraint programming approach (2009, unknown): “It implements the same propagation as the CP system, including iterative 4-support pruning and closednesspruning; itisbased onECLAT[28], a memory-e.cient depth-.rst miner.”
Turbo-charging vertical mining of large databases (2000, methodology): “FORC is based on the powerful technique of equivalence class clustering described in [11], but adds important new optimizations of its own.”
Efficient mining of frequent subgraphs in the presence of isomorphism (2003, methodology): “In [1], a depth first scheme is proposed following the idea of the Eclat association mining algorithm [11].”
Fast vertical mining using diffsets (2003, methodology): “Figure 7: Pseudo-code for dEclat To illustrate the power of diffset-based mining, we have integrated diffsets with Eclat [24, 22], a state-ofthe-art vertical mining algorithms.”
Data mining combined to the multicriteria decision analysis for the improvement of road safety: case of France (2019, methodology): “• Algorithms for extraction the frequent itemset: Apriori [9], AprioriTID [31], FPGrowth [32], Partition [33], Dic [34], Eclat [35], Apriori-Hybrid [36] and Relim [37].”
Algorithms for association rule mining — a general survey and comparison (2000, background): “In [28] the a lgor i thm E c l a t is introduced, t h a t combines DFS with tidlist intersections.”
Depth first generation of long patterns (2000, background): “Because of the inherent di culty of the itemset generation problem in terms of computational complexity, considerable research has been dev oted tow ards nding faster methods for generating large itemsets [6, 7, 11, 14, 15, 17, 18, 26, 29].”