The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Mining Generalized Association Rules in an Evolving Environment
|
Author(s): Wen-Yang Lin (National University of Kaohsiung, Taiwan)and Ming-Cheng Tseng (Institute of Information Engineering, Taiwan)
Copyright: 2009
Pages: 7
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.ch197
Purchase
|
Abstract
The mining of Generalized Association Rules (GARs) from a large transactional database in the presence of item taxonomy has been recognized as an important model for data mining. Most previous studies on mining generalized association rules, however, were conducted on the assumption of a static environment, i.e., static data source and static item taxonomy, disregarding the fact that the taxonomy might be updated as new transactions are added into the database over time, and as such, the analysts may have to continuously change the support and confidence constraints, or to adjust the taxonomies from different viewpoints to discover more informative rules. In this chapter, we consider the problem of mining generalized association rules in such a dynamic environment. We survey different strategies incorporating state-of-the-art techniques for dealing with this problem and investigate how to efficiently update the discovered association rules when there are transaction updates to the database along with item taxonomy evolution and refinement of support constraint.
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, Jingru Zhang.
© 2024.
26 pages.
|
Pua Shiau Chen.
© 2024.
21 pages.
|
Minh Tung Tran, Thu Trinh Thi, Lan Duong Hoai.
© 2024.
23 pages.
|
|
|