IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Constraint-Based Association Rule Mining

Constraint-Based Association Rule Mining
View Sample PDF
Author(s): Carson Kai-Sang Leung (The University of Manitoba, Canada)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch049

Purchase

View Constraint-Based Association Rule Mining on the publisher's website for pricing and purchasing information.

Abstract

The problem of association rule mining was introduced in 1993 (Agrawal et al., 1993). Since then, it has been the subject of numerous studies. Most of these studies focused on either performance issues or functionality issues. The former considered how to compute association rules efficiently, whereas the latter considered what kinds of rules to compute. Examples of the former include the Apriori-based mining framework (Agrawal & Srikant, 1994), its performance enhancements (Park et al., 1997; Leung et al., 2002), and the tree-based mining framework (Han et al., 2000); examples of the latter include extensions of the initial notion of association rules to other rules such as dependence rules (Silverstein et al., 1998) and ratio rules (Korn et al., 1998). In general, most of these studies basically considered the data mining exercise in isolation. They did not explore how data mining can interact with the human user, which is a key component in the broader picture of knowledge discovery in databases. Hence, they provided little or no support for user focus. Consequently, the user usually needs to wait for a long period of time to get numerous association rules, out of which only a small fraction may be interesting to the user. In other words, the user often incurs a high computational cost that is disproportionate to what he wants to get. This calls for constraint-based association rule mining.

Related Content

Girija Ramdas, Irfan Naufal Umar, Nurullizam Jamiat, Nurul Azni Mhd Alkasirah. © 2024. 18 pages.
Natalia Riapina. © 2024. 29 pages.
Xinyu Chen, Wan Ahmad Jaafar Wan Yahaya. © 2024. 21 pages.
Fatema Ahmed Wali, Zahra Tammam. © 2024. 24 pages.
Su Jiayuan, Zhang Jingru. © 2024. 26 pages.
Pua Shiau Chen. © 2024. 21 pages.
Minh Tung Tran, Thu Trinh Thi, Lan Duong Hoai. © 2024. 23 pages.
Body Bottom