Introduction to Clustering
- identifying types of clustering
- measuring similarity
- assessing multivariate normality
- using classification matrices
Preparation for Clustering
- preparing data for cluster analysis
- using variable clustering for variable selection
- using graphical clustering aids
- making elongated clusters more spherical
- viewing the impact of input standardization
Partitive Clustering
- k-means clustering for segmentation
- outlining the advantages of nonparametric clustering
Hierarchical Clustering
Assessing Clustering Results
- determining the number of clusters
- profiling a cluster solution
- scoring new observations
Cluster Analysis Case Study
- variable selection
- graphical exploration of selected variables
- hierarchical clustering and determining the number of clusters
- profiling the seven-cluster solution
- modelling cluster membership
- scoring the customer database
Canonical Discriminant Analysis (CDA)Plots
- canonical discriminant plots
Fuzzy Clustering
Assessing Multivariate Normality
- assessing multivariate normality
This course addresses SAS/STAT software.