Part III. SAS® Enterprise Miner

Table of Contents

14. Data Sources
Reading SAS dataset
Importing data from a CSV file
Importing data from a Excel file
15. Preprocessing
Constructing metadata and automatic variable selection
Vizualizing multidimensional data and dimension reduction by PCA
Replacement and imputation
16. Classification Methods 1
Classification by decision tree
Comparison and evaluation of decision tree classifiers
17. Classification Methods 2
Rule induction to the classification of rare events
18. Classification Methods 3
Logistic regression
Prediction of discrete target by regression models
19. Classification Methods 4
Solution of a linearly separable binary classification task by ANN and SVM
Using artificial neural networks (ANN)
Using support vector machines (SVM)
20. Classification Methods 5
Ensemble methods: Combination of classifiers
Ensemble methods: bagging
Ensemble methods: boosting
21. Association mining
Extracting association rules
22. Clustering 1
K-means method
Agglomerative hierarchical methods
Comparison of clustering methods
23. Clustering 2
Clustering attributes before fitting SVM
Self-organizing maps (SOM) and vector quantization (VQ)
24. Regression for continuous target
Logistic regression
Prediction of discrete target by regression models
Supervised models for continuous target
25. Anomaly detection
Detecting outliers