Premium
Fundamentals of association rules in data mining and knowledge discovery
Author(s) -
Zhang Shichao,
Wu Xindong
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.10
Subject(s) - association rule learning , k optimal pattern discovery , knowledge extraction , data mining , affinity analysis , association (psychology) , associative property , computer science , data science , mathematics , psychology , pure mathematics , psychotherapist
Association rule mining is one of the fundamental research topics in data mining and knowledge discovery that identifies interesting relationships between itemsets in datasets and predicts the associative and correlative behaviors for new data. Rooted in market basket analysis, there are a great number of techniques developed for association rule mining. They include frequent pattern discovery, interestingness, complex associations, and multiple data source mining. This paper introduces the up‐to‐date prevailing association rule mining methods and advocates the mining of complete association rules, including both positive and negative association rules. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 97‐116 DOI: 10.1002/widm.10 This article is categorized under: Algorithmic Development > Association Rules