Cluster Analysis

• K-Means Clustering
  Performs k-means clustering to determine a user-defined number of clusters; both variables (columns of the data matrix) and cases (rows of the data matrix) can be clustered. Final assignment of objects (cases, variables) to clusters are reported, and can be saved for further analyses.
• Tree Clustering (Joining)
  Performs tree clustering (joining); both variables (columns of the data matrix) and cases (rows of the data matrix) can be clustered. You can choose from among various distance measures, clustering algorithms, and amalgamation methods.
• Two-Way Joining Clustering
  Performs simultaneous two-way joining of cases (rows) and variables (columns) of a data matrix, and reports and plots the reordered data matrix.

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