Algorithms for association rule mining — a general survey and comparison

By Jochen Hipp, Ulrich Güntzer, G. Nakhaeizadeh. 2000

1,152 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

ORC: Ordered Rules for ClassificationA Discrete Optimization Approach to Associative Classification (2012, methodology): “…Apriori method by Agrawal and Srikant [1994], researchers have proposed many algorithms for frequent pattern mining that apply heuristic techniques to traverse the search space, which grows exponentially with the number of items in the dataset [Han et al., 2007, Hipp et al., 2000, Goethals, 2003].”
An efficient algorithm for discovering frequent subgraphs (2004, background): “support value is not extremely small [22], [18].”
Hierarchical Document Clustering using Frequent Itemsets (2003, methodology): “set mining algorithms have been proposed [6, 7] and can be employed to further improve the efficiency of our method.”
Biclustering algorithms for biological data analysis: a survey (2004, positive): “Their objective of finding all biclusters that, after column reordering, represent coherent evolutions of the symbols in the matrix is achieved by using a pattern discovery algorithm heavily inspired in sequential pattern mining algorithms [14].”
Knowledge graph refinement: A survey of approaches and evaluation methods (2016, methodology): “In data mining, association rule mining [37] is a method that analyzes the co-occurence of items in itemsets and derives association rules from those co-”
Relationship Between Student Engagement and Performance in E-Learning Environment Using Association Rules (2018, background): “The confidence of a rule can be thought of as the conditional probability of the rule [20].”
Frequent term-based text clustering (2002, background): “See [8] for an overview on association rule mining.”
Fuzzy association rules: general model and applications (2003, background): “See [24] for a recent survey of the problem.”
PR-Miner: automatically extracting implicit programming rules and detecting violations in large software code (2005, background): “The meaning of the rule is that if an itemset contains X , it also contains Y with probability of c [1, 15].”
Discovering Spatial Co-location Patterns: A Summary of Results (2001, background): “" Substantial literature is available on techniques for mining association rules [1, 2, 11, 19, 24, 25, 27].”
Mining Electronic Health Records: A Survey (2017, positive): “Hipp et al. [Hipp et al. 2000] presented a survey on efﬁcient algorithms widely used for association rule mining related tasks.”
Foundations and Advances in Data Mining (2005, methodology): “For instance, a face categorization system in which a single SVM classifier combines the real-valued output of a few classifiers, each trained to a different component of faces – such as eye and nose –, outperforms a single classifier trained on full images of faces ([25]).”