Regression analysis of the number of association rules

Authors:
Wei-Guo Yi;Ming-Yu Lu;Zhi Liu
Affiliations:
Department of Information Science and Technology, Dalian Maritime University, Dalian, PRC 116026 and Department of Software Institute, Dalian Jiaotong University, Dalian, PRC 116052;Department of Information Science and Technology, Dalian Maritime University, Dalian, PRC 116026;Department of Information Science and Technology, Dalian Maritime University, Dalian, PRC 116026
Venue:
International Journal of Automation and Computing
Year:
2011

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Abstract

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 coefficients to test the fitting effects of the equations and uses significance test to verify whether the coefficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coefficient 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.