JMP Clinical Starter | Pattern Discovery

Pattern Discovery
Click on a button corresponding to a pattern discovery process. Refer to the table below for guidance.
Creating a tree of observation (row) relationships with the option of clustering variables to create a two-way organization, choosing from a variety of methods
Creating optimally separated groups of observations (rows) resulting in groups with similar members
Tip: Consider running Data Standardize before clustering to ensure that the columns are all comparable.
Examining relationships among many quantitative variables, using orthogonal transformation to reduce potentially correlated variables into uncorrelated variables known as principal components
Computing and plotting distance or dissimilarity measures between observations (rows), and storing these measures in a square matrix output data set that can be used as input for the Multidimensional Scaling process
Tip: Input data for this process can be generated using the Distance Matrix and Clustering process.
Inferring association and potential causal relationships between a set of variables, plotting variables as nodes connected with line segments that vary in appearance based on partial correlations
Tip: A wide variety of plot types and interactive options are available. You are encouraged to explore them all.
See the JMP Clinical Starter main page for other process categories.