6 Factor Analysis
6.1 Motivating Example
6.2 General Theory of Factor Analysis
6.3 Principal Component Analysis
6.4 Principal Factor Analysis
7 Cluster Analysis
7.1 Motivating Examples
7.2 General Theory of Cluster Anaylsis
7.3 TwoStep Hierarchical Agglomerative Clustering
7.4 K-Means Partitioning Clustering
7.5 Auto Clustering
8 Classification Models
8.1 Motivating Examples
8.2 General Theory of Classification Models
8.3 Logistic Regression
8.4 Linear Discriminate Classification
8.5 Support Vector Machine
8.6 Neuronal Networks
8.7 K-Nearest Neighbor
8.8 Decision Trees
8.9 The Auto Classifier Node
9 Using R with the Modeler
9.1 Adventages of R with Modeler
9.2 Connecting with R
9.3 Test the SPSS Modeler Connection to R
9.4 Calcurating New Variables in R
9.5 Model Building in R
9.6 Modifying the Data Structure in R
9.7 Solutions
10 Imbalanced Data and Resampling Techniques
10.1 Characteristics of Imbalanced Datasets and Consequences
10.2 Resampling Techniques
10.3 Implementation in SPSS Modeler
10.4 Using R to Implement Balancing Methods
10.5 Excercise
10.6 Solutions
11 Case Study: Fault Detection in Semiconductor Manufacturing Process
11.1 Case Study Background
11.2 The Standard Process in Data MIning
11.3 Lessons Learned
11.4 Excercies
11.5 Solutions
12 Appendix
12.1 Data Sets Used in This Book
References
6.1 Motivating Example
6.2 General Theory of Factor Analysis
6.3 Principal Component Analysis
6.4 Principal Factor Analysis
7 Cluster Analysis
7.1 Motivating Examples
7.2 General Theory of Cluster Anaylsis
7.3 TwoStep Hierarchical Agglomerative Clustering
7.4 K-Means Partitioning Clustering
7.5 Auto Clustering
8 Classification Models
8.1 Motivating Examples
8.2 General Theory of Classification Models
8.3 Logistic Regression
8.4 Linear Discriminate Classification
8.5 Support Vector Machine
8.6 Neuronal Networks
8.7 K-Nearest Neighbor
8.8 Decision Trees
8.9 The Auto Classifier Node
9 Using R with the Modeler
9.1 Adventages of R with Modeler
9.2 Connecting with R
9.3 Test the SPSS Modeler Connection to R
9.4 Calcurating New Variables in R
9.5 Model Building in R
9.6 Modifying the Data Structure in R
9.7 Solutions
10 Imbalanced Data and Resampling Techniques
10.1 Characteristics of Imbalanced Datasets and Consequences
10.2 Resampling Techniques
10.3 Implementation in SPSS Modeler
10.4 Using R to Implement Balancing Methods
10.5 Excercise
10.6 Solutions
11 Case Study: Fault Detection in Semiconductor Manufacturing Process
11.1 Case Study Background
11.2 The Standard Process in Data MIning
11.3 Lessons Learned
11.4 Excercies
11.5 Solutions
12 Appendix
12.1 Data Sets Used in This Book
References