Class Description Generator

Context

• To manage groups of nodes, BayesiaLab offers Classes.

• Nodes can be added to Classes manually or automatically. For instance, the Variable Clustering function can assign nodes to new Classes representing latent factors. By default, newly-created Classes have generic names, such as [Factor_0], which carries no meaning.

• Finding suitable descriptions for Classes can be time-consuming.

• The Class Description function can assist you in finding meaningful summaries of a Class of nodes.

Using Class Description Generator for Groups of Nodes

• With the Hellixia Class Description Generator, we can quickly find a useful description for a subset of nodes we select.

• In our example, we have a large number of nodes from an auto buyer satisfaction survey.

• We are interested in a subset of nodes related to the quality perception of the vehicle interior, i.e.:

  • Interior Colors

  • Quality of Interior Materials

  • Interior Trim & Finish

  • Quality of Seat Materials

• Select these nodes node of interest.

• Then select Main Menu > Hellixia > Class Description.

• Specify a Context, if applicable.

• Indicate by ticking the checkboxes where the subject matter is stored, i.e., Node Name, Node Long Name, or Node Comment. Check all that apply.

• Clicking OK starts generating the Class Description.

• The chime confirms when the process is complete.

• Opening the Class Editor shows the Class Description that was generated.

• Select Graph Contextual Menu > Edit Classes

• The Description column shows the newly-generated Class Description.

Workflow Illustration

Using the Class Description Generator from within the Class Editor

• BayesiaLab's Clustering function produces new Factors and associated Classes.

• So, having a dozen or more new Classes is quite common in this context.

• By default, the newly-generated Classes have generic and non-informative names, like [Factor_0], [Factor_1], etc.

• Given that the Factors and Classes are meant to represent meaningful concepts, naming them is important but can be tedious.

• In the following example, 57 Factors (and Classes) were created from 240 manifest nodes. Each manifest node measures the degree of agreement or disagreement with statements in a personality test, such as, "I get angry easily" or "I remain calm under pressure."

• These original statements are included as Node Comments with every node.

Workflow Illustration