The main result of principal component analysis is the principal component coordinates of individual records, which can be used in the further data analysis and visualization. Figure 15.8. Scatterplit of the Iris dataset using the first two principal components Interpretation of the results The experiment shows that how we can display high-dimensional data sets and perform dimension reduction. In our experiment, the original data set that can not be displayed using the standard scatterplot, is 4-dimensional managed to reduce to dimensions such that the percent of the information contained in the data is 2 95 preserved. Video Workflow sas_preproc_exp2.xml Keywords principal components analysis (PCA) parallel axis Operators Data Source Graph Explore Principal Components 3. Replacement and imputation Description In this experiment, we demonstrate by the help of the dataset how to modify the Congressional Voting Records values of attributes by the operator and then how to impute the missing value by the Replacement Impute operator. The replacement of missing values each variable can be carried out independently of the others and to interact with the target variable by fitting a decision tree. Input [] Congressional Voting Records Output By the operator we can set the substitution of discrete and continuous variables separately. Replacement Figure 15.9. The replacement wizard
The main result of principal component analysis is the principal component coordinates of individual records, which can be used in the further data analysis and visualization. Figure 15.8. Scatterplit of the Iris dataset using the first two principal components Interpretation of the results The experiment shows that how we can display high-dimensional data sets and perform dimension reduction. In our experiment, the original 4-dimensional data set that can not be displayed using the standard scatterplot, is managed to reduce to 2 dimensions such that the 95 percent of the information contained in the data is preserved. Video Workflow sas_preproc_exp2.xml Keywords principal components analysis (PCA) parallel axis Operators Data Source Graph Explore Principal Components 3. Replacement and imputation Description In this experiment, we demonstrate by the help of the Congressional Voting Records dataset how to modify the values of attributes by the Replacement operator and then how to impute the missing value by the Impute operator. The replacement of missing values each variable can be carried out independently of the others and to interact with the target variable by fitting a decision tree. Input Congressional Voting Records [] Output By the Replacement operator we can set the substitution of discrete and continuous variables separately. Figure 15.9. The replacement wizard
2011-0103_06_case_studies_in_data_mining.docx