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Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Mining Association Rules

Mining Association Rules
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Author(s): Mihai Gabroveanu (University of Craiova, Romania)
Copyright: 2009
Pages: 27
Source title: Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches
Source Author(s)/Editor(s): Adrian Giurca (Brandenburg Technology University at Cottbus, Germany), Dragan Gasevic (Athabasca University, Canada) and Kuldar Taveter (University of Melbourne, Australia)
DOI: 10.4018/978-1-60566-402-6.ch027


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During the last years the amount of data stored in databases has grown very fast. Data mining, also known as knowledge discovery in databases, represents the discovery process of potentially useful hidden knowledge or relations among data from large databases. An important task in the data mining process is the discovery of the association rules. An association rule describes an interesting relationship between different attributes. There are different kinds of association rules: Boolean (crisp) association rules, quantitative association rules, fuzzy association rules, etc. In this chapter, we present the basic concepts of Boolean and the fuzzy association rules, and describe the methods used to discover the association rules by presenting the most important algorithms.

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