Regression Analysis of the Number of Association Rules  

作　　者：Wei-Guo Yi Ming-Yu Lu Zhi Liu 

机构地区：[1]Department of Information Science and Technology, Dalian Maritime University, Dalian 116026, PRC [2]Department of Software Institute, Dalian Jiaotong University, Dalian 116052, PRC

出　　处：《International Journal of Automation and computing》2011年第1期78-82,共5页国际自动化与计算杂志（英文版）

基　　金：supported by the National Natural Science Foundation of China (No. J07240003, No. 60773084, No. 60603023);National Research Fund for the Doctoral Program of Higher Education of China (No. 20070151009)

摘　　要：The typical model, which involves the measures： support, confidence, and interest, is often adapted to mining association rules. In the model, the related parameters are usually chosen by experience; consequently, the number of useful rules is hard to estimate. If the number is too large, we cannot effectively extract the meaningful rules. This paper analyzes the meanings of the parameters and designs a variety of equations between the number of rules and the parameters by using regression method. Finally, we experimentally obtain a preferable regression equation. This paper uses multiple correlation coeficients to test the fitting efiects of the equations and uses significance test to verify whether the coeficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coeficient will be chosen as the optimally fitted equation. With the selected optimal equation, we can predict the number of rules under the given parameters and further optimize the choice of the three parameters and determine their ranges of values.The typical model, which involves the measures： support, confidence, and interest, is often adapted to mining association rules. In the model, the related parameters are usually chosen by experience; consequently, the number of useful rules is hard to estimate. If the number is too large, we cannot effectively extract the meaningful rules. This paper analyzes the meanings of the parameters and designs a variety of equations between the number of rules and the parameters by using regression method. Finally, we experimentally obtain a preferable regression equation. This paper uses multiple correlation coeficients to test the fitting efiects of the equations and uses significance test to verify whether the coeficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coeficient will be chosen as the optimally fitted equation. With the selected optimal equation, we can predict the number of rules under the given parameters and further optimize the choice of the three parameters and determine their ranges of values.

关 键 词：Association rules regression analysis multiple correlation coeficients INTEREST SUPPORT confidence. 

分 类 号：O212.1[理学—概率论与数理统计] TP311.13[理学—数学]