Linear Correlation Discovery in Databases: A Data Mining Approach
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.
Recommended Citation
Chiang, R. H., Chua, C. E., & Lim, E. (2005). Linear Correlation Discovery in Databases: A Data Mining Approach. Data and Knowledge Engineering, 53(3), pp. 311-337. Elsevier B.V..
The definitive version is available at https://doi.org/10.1016/j.datak.2004.09.002
Department(s)
Business and Information Technology
Keywords and Phrases
Association measurement; Data mining; Knowledge discovery in database; Linear correlation
International Standard Serial Number (ISSN)
0169-023X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2005 Elsevier B.V., All rights reserved.
Publication Date
01 Jun 2005
