Regression Analysis of the Number of Association Rules

作     者：Wei-Guo Yi Ming-Yu Lu Zhi Liu 

作者机构：Department of Information Science and Technology Dalian Maritime University Dalian 116026 PRC Department of Software Institute Dalian Jiaotong University Dalian 116052 PRC 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2011年第8卷第1期

页      面：78-82页

核心收录：

学科分类：12[管理学] 02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 0802[工学-机械工程] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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) 

主　　题：Association rules regression analysis multiple correlation coeficients interest support confidence. 

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.