Fast Algorithms for Mining Association Rules

By Rakesh Agrawal, Rayadurgam Srikant. 1998

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Citations in the corpus

Privacy Preserving Data Mining (2015, background): “The medical field has much to gain by pooling data for research; as can even competing businesses with mutual interests.”
Interpretable & Explorable Approximations of Black Box Models (2017, neutral): “2 M— i=1 „qi[si” width„e” numpreds„R”= M˝ i=1 width„si”+width„qi” numdsets„R”= jdset„R”jwheredset„R”= M— i=1 qi f eatureoverlap„R”= ˝ q2dset„R” M˝ i=1 f eatureoverlap„q; si” algorithm such as apriori [1] can be used to generate the candidate sets of conjunctions of predicates. Without any input from”
Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “Since the introduction of the Apriori method (Agrawal and Srikant, 1994), researchers have proposed many algorithms for frequent pattern mining, and rule learning. Reviews are given by Han et al (2007); Hipp et al (2000); Goethals (2003). There is a large body of literature on interestingness measur”
Boolean Decision Rules via Column Generation (2018, background): “(4) The objective function (1) is the Hamming loss as described.”
An optimization approach to learning falling rule lists (2017, methodology): “In our experiments, we have chosen the FP-growth algorithm [11] to mine antecedents, but there is a vast literature on rule mining algorithms (e.g. [1, 12, 15]), and any of these can be used to produce antecedents for our algorithm.”
CPAR: Classification based on Predictive Association Rules (2003, methodology): “In recent years, a new approach called associative classification [7, 6] is proposed to integrate association rule mining [1] and classification.”
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration (2016, background): “Such patterns can be obtained by running an off-the-shelf frequent pattern mining algorithm such as Apriori (Agrawal, Srikant, and others 1994) on X .”
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data (2003, methodology): “The observed tendencies in Figs.10,11 and 13 are almost identical with the properties of Apriori algorithm (Agrawal and Srikant, 1994).”
Robustness of collaborative recommendation based on association rule mining (2007, methodology): “Considering each user profile as a transaction, it is possible to use the Apriori algorithm [1] and generate association rules for groups of commonly liked items.”
Detecting the Hate Code on Social Media (2017, methodology): “Next, we ran the Apriori algorithm for frequent itemset mining (Agrawal and Srikant 1994) on a randomly selected set of tweets that we identiﬁed as being hateful.”
Discriminative Frequent Pattern Analysis for Effective Classification (2007, background): “Frequent pattern mining has been a focused theme in data mining research with a large number of scalable methods proposed for mining various kinds of patterns including itemsets [2, 10, 27], sequences [3, 16, 26] and graphs [11, 22].”
Mining, indexing, and querying historical spatiotemporal data (2004, methodology): “A version of the well-known Apriori algorithm [1] was adapted for the problem of ﬁnding patterns of the form *AB**C , where A , B , and C are speciﬁc symbols (e.g., event types) and * could be any symbol ( T = 6, in this example).”
Mining version histories to guide software changes (2005, methodology): “The Apriori Algorithm takes a minimum support count (or minimum support) and a minimum confidence and computes the set of all association rules that are above both thresholds.”
Mining association rules with multiple minimum supports (1999, background): “Efficient algorithms for finding large itemsets are based on level-wise search [3] If an itemset is not large at level k-l, it is discarded as any addition of items to the set cannot be large (downward closure property).”