Pattern Discovery
Click on a button corresponding to a pattern discovery process. Refer to the table below for guidance.
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Choose this process for... |
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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 |
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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. |
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Examining relationships among many quantitative variables, using orthogonal transformation to reduce potentially correlated variables into uncorrelated variables known as principal components |
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Visualizing row-level intensity measurements for individual samples via Parallel Plots, with the option of placing measurements and plots into groups as defined in an Experimental Design Data Set (EDDS) |
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Computing and testing the significance of all pairwise correlations between two sets of numeric variables, and visualizing these correlations using a Heat Map and Dendrogram |
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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 |
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Estimating the coordinates of a set of objects in a space of specified dimensionality (using distance matrix input) and creating a 2-D or 3-D Scatterplot of these coordinates Tip: Input data for this process can be generated using the Distance Matrix and Clustering process. |
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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 The JMP Genomics Starter main page for other process categories.