Completion of Quantitative Missing Values Using Principal Component Analysis Method

• Main Authors: Choa, syie-yi, 趙士儀
• Other Authors: Chien-Lung Chan
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/19881724073002520891

碩士 === 元智大學 === 資訊管理研究所 === 88 === Missing value is an important issue for the analysts during the process of data mining process. The problem is what kind of methods will be suitable to complete those missing values. A current approach filling the missing values in decision trees is using the most common value. This value can be chosen either from the whole data set or from data sets constructed for the classification task. An alternative method, MVC (Missing Values Completion), uses the association rules, discovered with RAR (Robust Association Rules) to mine databases containing multiple missing values, allows to use it for the missing values problem. However, the MVC method is not suitable for mining the quantitative association rules. Our approach is using the concept of PCA (Principal Component Analysis) and eigenvectors figuring out the principal components from quantitative attributes, and using the PCs (Principal Components) handling the missing values problem. The study demonstrated that the principal component analysis model can be used to administer a more reliable and valid values then the regression, with enough and representative data records. For the application, this approach is not only enhancing the validity of the data cleaning progress in KDD, but also predicting correctly the missing values in new data records that is according to the historic data records.