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

Data Mining Association Rules for Making Knowledgeable Decisions

Data Mining Association Rules for Making Knowledgeable Decisions
View Sample PDF
Author(s): A.V. Senthil Kumar (CMS College of Science and Commerce, India)and R. S.D. Wahidabanu (Govt. College of Engineering, India)
Copyright: 2009
Pages: 11
Source title: Data Mining Applications for Empowering Knowledge Societies
Source Author(s)/Editor(s): Hakikur Rahman (Ansted University Sustainability Research Institute, Malaysia)
DOI: 10.4018/978-1-59904-657-0.ch003

Purchase

View Data Mining Association Rules for Making Knowledgeable Decisions on the publisher's website for pricing and purchasing information.

Abstract

This chapter describes two techniques used to explore frequent large itemsets in the database. In the first technique called “closed directed graph approach,” the algorithm scans the database once making a count on possible 2-itemsets from which only the 2-itemsets with a minimum support are used to form the closed directed graph which explores possible frequent large itemsets in the database. In the second technique, dynamic hashing algorithm, large 3-itemsets are generated at an earlier stage which reduces the size of the transaction database after trimming and the cost of later iterations will be less. Furthermore the authors hope that these techniques help researchers not only to understand about generating frequent large itemsets, but also assist with the understanding of finding association rules among transactions within relational databases.

Related Content

. © 2023. 34 pages.
. © 2023. 15 pages.
. © 2023. 15 pages.
. © 2023. 18 pages.
. © 2023. 24 pages.
. © 2023. 32 pages.
. © 2023. 21 pages.
Body Bottom