A Performance Study of Data Mining Techniques: Multiple Linear Regression vs. Factor Analysis
The growing volume of data usually creates an interesting challenge for the need of data analysis tools that discover regularities in these data. Data mining has emerged as disciplines that contribute tools for data analysis, discovery of hidden knowledge, and autonomous decision making in many application domains. The purpose of this study is to compare the performance of two data mining techniques viz., factor analysis and multiple linear regression for different sample sizes on three unique sets of data. The performance of the two data mining techniques is compared on following parameters like Mean Square Error (MSE), R-square, R-Square adjusted, condition number, Root Mean Square Error (RMSE), number of variables included in the prediction model, modified coefficient of efficiency, F-value, and test of normality.