JMP Genomics 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 Genomics Starter main page for other process categories.