Linear correlation discovery in databases: a data mining approach

Authors:
Roger H. L. Chiang;Chua Eng Huang Cecil;Ee-Peng Lim
Affiliations:
Information Systems Department, College of Business, University of Cincinnati, P.O. Box 210211, Cincinnati, OH;Nanyang Business School, Nanyang Technological University, Singapore 639798, Singapore;Center for Advanced Information Systems, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore
Venue:
Data & Knowledge Engineering
Year:
2005

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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.