Converting Datasets to Structures
The Convert Dataset to Structure task can produces a number of structures from a dataset in input. Those structures include replacement rules, autoregressive models, cluster labels, clusters, discretization cutoffs, frequent itemsets, frequent sequences, results, rules, models, and pca eigenvectors.
There are many reasons why you may want to convert structures, here are a few examples:
to quickly add a large number of heuristic rules into a flow, by inserting the rules into a table, which can then be imported into a flow as a dataset, and then converted into a ruleset.
to reconvert structures that were previously converted into a dataset, using the Convert Structure to Dataset task, in order to perform in-depth analysis in the Data Manager, back into their original format.
to create a model from a dataset, which can then be used in an Apply Model task to derive its responses in correspondence of given samples.
The more specific Convert Dataset to Ruleset (and vice-versa) and Convert Dataset to Model (and vice-versa) tasks are still available for backward compatibility, but they are fully substituted by this new set of generic structure conversion tasks.
you must have created a flow;
the dataset you want to convert must be correctly formed for the new structure
Drag and drop the Convert Dataset to Structure task onto the stage.
Connect a task that contains an existing dataset to the Convert Dataset to Structure task.
Double click the task and select the required structure. The only structure that requires additional parameters are rules, which require the parameters explained in the table below.
Save and compute the task.
Convert Dataset to Ruleset options
Rule ID atttibute
Select the attribute in the dataset which contains the ID for each rule. These numbers must be unique and consecutive. If this is not the case, leave this option blank and new numbers will be assigned to the rows.
Rule output name attribute
Select the attribute in the dataset that contains the output attributes.
Rule output value attribute
Select the attribute in the dataset that contains the output attribute values.
Rule covering attribute
Select the attribute in the dataset that contains the covering value.
Rule error attribute
Select the attribute in the dataset that contains the error value.
Rule conditions (NOMINAL)
Drag and drop here all the attributes that represent rule conditions.