数据采掘技术的性能研究：多元线性回归与因子分析
A Performance Study of Data Mining Techniques: Multiple Linear Regression vs. Factor Analysis
摘要：
不断增长的数据量通常会对数据分析工具的需求提出一个有趣的挑战，这些数据分析工具可以发现这些数据中的错误。数据采掘已成为一门学科，在许多应用领域中为数据分析、发现隐藏知识和自主决策提供了工具。本研究的目的是比较两种数据采掘技术的性能，即，因子分析和多元线性回归的不同样本量的三个独特的数据集。两种数据采掘技术的性能进行了比较以下参数，如均方误差（MSE），R-平方，R-平方调整，条件数，均方根误差（RMSE），预测模型中包含的变量数，修正系数的效率，F值，和正态性检验。这些参数已使用各种数据采掘工具，如SPSS，XLstat，Stata和MS-Excel计算。可以看出，对于所有给定的数据集，因子分析优于多元线性回归。但预测精度的绝对值在三个数据集之间存在差异，这表明数据分布和数据特征在选择正确的预测技术时起着重要作用。
英文摘要：
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.
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