An implementation of the FP-growth algorithm

By Christian Borgelt. 2005

395 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems, Algorithms & Complexity

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Citations in the corpus

Boolean Decision Rules via Column Generation (2018, background): “The parameters of BRS and FPGrowth (6), the frequent rule miner that BRS relies on, were set as recommended in (48) and the associated code (see SM for details).”
Learning Optimized Or's of And's (2015, methodology): “In our implementation, we use FP-growth in python (Borgelt, 2005) that takes the binary coded data, and user specified minimum support and maximum length, to generate patterns that satisfy the two requirements.”
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, methodology): “To do this, we use the FP-growth algorithm [8], which can in practice be replaced with any desired frequent pattern-mining method.”
Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test (2015, methodology): “We used FPGrowth (Borgelt 2005) to extract decision rules from our data that predict each of our conditions (memory impairment disorders, vascular cognitive disorders, PD).”
RAPID: Rapid and Precise Interpretable Decision Sets (2019, methodology): “It has been empirically shown that FP-Growth has orders of magnitudes lower running times than the Apriori algorithm [33].”
An interpretable model for stroke prediction using rules and Bayesian analysis (2014, methodology): “In our experiments, the features were categorical so we used the FP-Growth algorithm [1] which finds all itemsets that satisfy constraints on minimum support and maximum cardinality.”
Bayesian Rule Sets for Interpretable Classification (2016, methodology): “1) Reducing rule space: First, we used FP-Growth [23] to generate candidate rules.”
World-scale mining of objects and events from community photo collections (2008, methodology): “We use an implementation of the fpgrowth [4] algorithm to mine the frequent itemsets for each cluster, using a minimal support threshold of 0 .”
Large scale mining and retrieval of visual data in a multimodal context (2008, methodology): “We use an implementation [Borgelt, 2005] of the FP-Growth algorithm to mine the frequent itemsets for each cluster, using a minimal support threshold of 0.”
An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis (2013, methodology): “We consider a collection of datasets from the UCI Machine Learning Repository (Bache and Lichman, 2013) that are frequently used in the benchmark dataset studies of machine learning papers: Tic-Tac-Toe Endgame, Mammographic Mass (Elter et al.”
A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, methodology): “Algorithm 1 Inference algorithm. procedure SIMULATED ANNEALING(Niter) A ← FP-Growth(S) A[0] ← a randomly generated rule set Amax ← A[0] for t = 0→ Niter do A ← {a ∈ A, supp(a) ≥ C [t]} (C [t] is from (12) or (14), depending on the model choice) S ← misclassified examples by A[t] (Find misclassified ”
Building Interpretable Classifiers with Rules using Bayesian Analysis (2012, methodology): “In our experiments we used the FP-Growth algorithm [3] which finds all itemsets that satisfy constraints on minimum support and maximum cardinality.”