A Performance Study of Data Mining Techniques: Multiple Linear Regression vs. Factor Analysis

TL;DR

This study compares the performance of factor analysis and multiple linear regression in data mining across different datasets, revealing that factor analysis generally outperforms regression, with effectiveness depending on data characteristics.

Contribution

It provides a comparative analysis of two data mining techniques on multiple datasets, highlighting the impact of data characteristics on their performance.

Findings

Factor analysis outperforms multiple linear regression in all datasets.

Prediction accuracy varies significantly with data distribution and characteristics.

Data mining tools like SPSS and Excel were used for evaluation.

Abstract

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. These parameters have been computed using various data mining tools like SPSS, XLstat, Stata, and MS-Excel. It is seen that for all the given dataset, factor analysis outperform multiple linear regression. But the absolute value of prediction accuracy varied between the three datasets indicating that the data distribution and data characteristics play a major role in choosing the correct prediction technique.