Fast Algorithms for Mining Association Rules in Large Databases

By Rakesh Agrawal, Rayadurgam Srikant. 1994

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Citations in the corpus

Towards Better Analysis of Deep Convolutional Neural Networks (2016, methodology): “To mine the closed item sets, we adopt the widely used Apriori, an algorithm for frequent item set mining [2].”
Interpreting tree ensembles with inTrees (2014, methodology): “Association rule analysis [2,21] discovers associations between items in transaction data sets, where one transaction consists of one or multiple items.”
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations (2019, methodology): “econd, ﬁdelity is measured as before—e.g., the accuracy relative to B. We use f 5(R) to denote the ﬁdelity of R. Finally, to construct the search space, we use frequent itemset mining (e.g., apriori (Agrawal and Srikant 2004)) to generate two sets of potential if conditions (i.e., sets of conjunctio”
Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, methodology): “Our approach IDS as well as the baselines BDL and CBA utilize the Apriori algorithm [2] for generation of candidate itemsets.”
Playing for Data: Ground Truth from Computer Games (2016, positive): “Instead of specifying such cases by hand, we discover them automatically during the labeling process via association rule mining [1].”
A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I (2014, methodology): “A commonly used algorithm for generating frequent itemsets is the a priori algorithm [26], [27].”
A review on time series data mining (2011, background): “Association rule mining (Agrawal et al., 1993b; Agrawal and Srikant, 1994) is one of the most well known algorithms.”
Sequence-Aware Recommender Systems (2018, background): “Early Association Rule Mining approaches [5] focused on identifying items that frequently co-occur in the same transaction, regardless of the order of their appearance.”
Differential Privacy for Deep and Federated Learning: A Survey (2022, methodology): “…noisy dataset for frequent itemset learning; ﬁrst, the algorithm takes an integer k and distinguish the top k most frequent items by running frequent itemset mining algorithm [103], after that, the algorithm builds an (cid:15) -differentially private FP-tree [104] that is released to the analyzer.”
New Algorithms for Enumerating All Maximal Cliques (2004, background): “From both theoretical and practical points of view, it has taken on increasing importance in many scientific fields such as artificial intelligence [10, 20], graph theory [14, 19, 21], operations research [16], data mining [2, 3], web mining [15], bioinformatics, and computational linguistics.”
What Did I Do Wrong in My MOBA Game? Mining Patterns Discriminating Deviant Behaviours (2016, unknown): “Consider a set of transactions, where each transaction is composed of a set of items (in the original problem formulation, a set of items bought in a supermarket [5]).”