Publication Type
Journal Article
Version
publishedVersion
Publication Date
2005
Abstract
Very little research in knowledge discovery has studied how to incorporate statistical methods to automate linear correlation discovery (LCD). We present an automatic LCD methodology that adopts statistical measurement functions to discover correlations from databases’ attributes. Our methodology automatically pairs attribute groups having potential linear correlations, measures the linear correlation of each pair of attribute groups, and confirms the discovered correlation. The methodology is evaluated in two sets of experiments. The results demonstrate the methodology’s ability to facilitate linear correlation discovery for databases with a large amount of data.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
Data and Knowledge Engineering
Volume
53
Issue
3
First Page
311
Last Page
337
ISSN
0169-023X
Identifier
10.1016/j.datak.2004.09.002
Publisher
Elsevier
Citation
CHUA, Cecil; CHIANG, Roger Hsiang-Li; and LIM, Ee Peng.
Linear correlation discovery in databases: A data mining approach. (2005). Data and Knowledge Engineering. 53, (3), 311-337.
Available at: https://ink.library.smu.edu.sg/sis_research/45
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1016/j.datak.2004.09.002
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons