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http://dx.doi.org/10.7465/jkdi.2016.27.3.629

Generally non-linear regression model containing standardized lift for association number estimation  

Park, Hee Chang (Department of Statistics, Changwon National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.27, no.3, 2016 , pp. 629-638 More about this Journal
Abstract
Among data mining techniques, the association rule is one of the most used in the real fields because it clearly displays the relationship between two or more items in large databases by quantifying the relationship between the items. There are three primary quality measures for association rule; support, confidence, and lift. We evaluate association rules using these measures. The approach taken in the previous literatures as to estimation of association rule number has been one of a determination function method or a regression modeling approach. In this paper, we proposed a few of non-linear regression equations useful in estimating the number of rules and also evaluated the estimated association rules using the quality measures. Furthermore we assessed their usefulness as compared to conventional regression models using the values of regression coefficients, F statistics, adjusted coefficients of determination and variation inflation factor.
Keywords
Confidence; generally non-linear regression equation; interestingness measure; standardized lift; support;
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Times Cited By KSCI : 16  (Citation Analysis)
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