Fast Discovery of Association Rules

By Rakesh Agrawal, Heikki Mannila, Rayadurgam Srikant, Hannu (TT) Toivonen, A. I. Verkamo. 1996

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Citations in the corpus

Learning Optimal Decision Trees Using Caching Branch-and-Bound Search (2020, methodology): “…set of ideas than the MIP-based approaches: it treats the paths of a decision tree as itemsets , and uses ideas from the itemset mining literature (Agrawal et al. 1996) to search through the space of possible paths efﬁciently, performing dynamic programming over the itemsets to construct an…”
A fast APRIORI implementation (2003, background): “We assume that the reader is familiar with APRIORI [2] and we turn our attention to its central data structure.”
Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, background): “This property is already well known from association rule learning (Agrawal et al. 1995; Goethals 2005; Hipp et al. 2000) and subgroup discovery (Kralj Novak et al.”
New Algorithms for Enumerating All Maximal Cliques (2004, background): “The problems have not only theoretical interest, but also a number of potential applications in many areas (e.g., [15, 2, 3 ]).”
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach (2004, unknown): “Most of the previous studies, such as Agrawal and Srikant (1994), Mannila et al. (1994), Agrawal et al. (1996), Savasere et al. (1995), Park et al. (1995), Lent et al. (1997), Sarawagi et al. (1998), Srikant et al. (1997), Ng et al. (1998) and Grahne et al. (2000), adopt an Apriori-like approach,…”
Fast vertical mining using diffsets (2003, methodology): “We compare the new dEclat method against Eclat [22], the classic Apriori [1] and recent Viper [20] algorithm.”
Krimp: mining itemsets that compress (2011, background): “The best known instance of theory mining is frequent set mining (Agrawal et al. 1996); this is the problem we will consider throughout this paper.”
Efficient algorithms for mining closed itemsets and their lattice structure (2005, methodology): “We also include a comparison with the base Apriori algorithm [1] for mining all itemsets.”
A Sketch Algorithm for Estimating Two-Way and Multi-Way Associations (2007, methodology): ““Market-basket” analysis and association rules (Agrawal, Imielinski, and Swami 1993; Agrawal and Srikant 1994; Agrawal et al. 1996; Hastie, Tibshirani, and Friedman 2001, Chapter 14.2) are useful tools for mining commercial databases.”
LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets (2004, background): “The first group is so called apriori or level-by-level algorithms [1, 2].”
Fast rule mining in ontological knowledge bases with AMIE+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{docu (2015, methodology): “Similar to APRIORI algorithms [4], it performs a breadth-first search in order to find frequent patterns.”
Optimal constraint-based decision tree induction from itemset lattices (2010, methodology): “The algorithm attempts to enumerate more promisin g regions earlier, and prunes unpromising regions of the search space. However, the algor ithm does not exploit dynamic programming strategies. It was applied both on conventiona l learning problems as problems with cost constraints. Blanchard, Scḧ a”