由於電腦科技的進步，更易收集與儲存龐大的資料。然而，資料分析與萃取，傳統技術以統計分析為代表，舉凡統計學內所含之敘述統計（Descriptive Statistics）、迴歸分析（Regression Analysis）、類別資料分析（Categorical Data Analysis），以及多變量分析（Multivariate Analysis）等。近年來，資料採礦方法的崛起，改良傳統統計分析方法，試圖分析高維度以及筆數龐大的資料。在本研究中，使用統計分析方法與資料採礦方法，配合不同類型的實際資料，進行分析與比較，再以重複抽樣的方法，進行資料切割與交叉驗證，並配合評估準則，深入探討資料採礦方法。因此，在本研究中，經由對資料重複抽樣的模擬結果，可以得知，雖然不同類型的資料，分類的情形不近相同；但羅吉斯迴歸分析（Logistic Regression Analysis），整體而言是比較好的分類方法。另外，對於高維度資料而言，重複抽樣是比較可行的分析方法。

As computer technology is fast growing, it's easier to save and gather huge data. However, analyzing and picking data take the statistical methods traditionally, for examples, Descriptive Statistics, Regression Analysis, Categorical Data Analysis, Multivariate Analysis, and so on. In recent years, the data mining method improves the traditional statistical methods and attempts to analyze high-dimensional and enormous data. In this study, we employ statistical methods and data mining methods with different types of actual data to analyze and compare, and then resampling data to segment and cross-validate. Finally, with assessing criteria, we investigate profoundly each data mining method by simulating results of resampling data. The study shows that the classifications of different types of data are not exactly the same. However, Logistic Regression Analysis is a better classification as a whole. In addition, the resampling method is a better analysis for the high dimensional data.

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