Linear correlation discovery in databases: a data mining approach

被引：11

作者：

Chiang, RHL

Cecil, CEH

Lim, EP

机构：

[1] Univ Cincinnati, Coll Business, Dept Informat Syst, Cincinnati, OH 45221 USA

[2] Nanyang Technol Univ, Nanyang Business Sch, Singapore 639798, Singapore

[3] Nanyang Technol Univ, Sch Comp Engn, Ctr Adv Informat Syst, Singapore 639798, Singapore

来源：

DATA & KNOWLEDGE ENGINEERING | 2005年 / 53卷 / 03期

关键词：

knowledge discovery in database; linear correlation; association measurement; data mining;

D O I：

10.1016/j.datak.2004.09.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (c) 2004 Elsevier B.V. All rights reserved.

引用

页码：311 / 337

页数：27

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