Linear correlation discovery in databases: A data mining approach

• Main Authors: CHUA, Cecil, CHIANG, Roger Hsiang-Li, LIM, Ee Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2005
• Subjects: Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/45; https://ink.library.smu.edu.sg/context/sis_research/article/1044/viewcontent/1_s2.0_S0169023X04001521_main.pdf
• Institution: Singapore Management University

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.