Process Flow Diagram Logic

Exploring Nodes

The Distribution Explorer node is an advanced visualization tool that enables you to quickly and easily explore large volumes of data in multidimensional histograms. You can view the distribution of up to three variables at a time with this node. When the variable is binary, nominal, or ordinal, you can select specific values to exclude from the chart. To exclude extreme values for interval variables, you can set a range cutoff. The node also generates summary statistics for the charting variables.
The Multiplot node is another visualization tool that enables you to explore larger volumes of data graphically. Unlike the Insight or Distribution Explorer nodes, the Multiplot node automatically creates bar charts and scatter plots for the input and target variables without requiring you to make several menu or window item selections. The code that is created by this node can be used to create graphs in a batch environment, whereas the Insight and Distribution Explorer nodes must be run interactively.
Use the Insight node to open a SAS/INSIGHT session. SAS/INSIGHT software is an interactive tool for data exploration and analysis. With it, you explore data through graphs and analyses that are linked across multiple windows. You can analyze univariate distributions, investigate multivariate distributions, and fit explanatory models by using generalized linear models.
Use the Association node to identify association relationships within the data. For example, if a customer buys a loaf of bread, how likely is the customer to also buy a gallon of milk? You also use the Association node to perform sequence discovery if a time stamp variable (a sequence variable) is present in the data set. Binary sequences are constructed automatically, but you can use the Event Chain Handler to construct longer sequences that are based on the patterns that the algorithm discovered.
You use the Variable Selection node to evaluate the importance of input variables in predicting or classifying the target variable. To preselect the important inputs, the Variable Selection node uses either an R-Square or a Chi-Square selection (tree based) criterion. You can use the R-Square criterion to remove variables in hierarchies, remove variables that have large percentages of missing values, and remove class variables that are based on the number of unique values. The variables that are not related to the target are set to a status of rejected. Although rejected variables are passed to subsequent nodes in the process flow diagram, these variables are not used as model inputs by a more detailed modeling node, such as the Neural Network and Tree nodes. You can reassign the status of the input model variables to rejected in the Variable Selection node.
Use the Link Analysis node to transform data from differing sources into a data model that can be graphed. The data model supports simple statistical measures, presents a simple interactive graph for basic analytical exploration, and generates cluster scores from raw data that can be used for data reduction and segmentation.

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