• Release Notes Rulex 4
  • Rulex Development Manual
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  • How-to articles
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  • Rulex Development Manual
    • Prerequisites
    • Getting to Know Rulex
      • Rulex Development Environment
        • Database Settings
        • Stage Settings
        • Command and Toolbar functions
        • Datasets and Attributes
      • Overview on Rulex Tasks
        • Saving and Computing Tasks
        • Selecting Attributes in Tasks
        • Analyzing Task Performance
        • Searching Tasks
      • How is the Manual Structured?
    • Creating Processes
      • Versioning Processes
      • Creating a Process from Scratch
      • Checking the Validity of Processes with the Workflow Review Tool
        • Creating an Excel Validation File
        • Mapping Tasks to Categories
    • Importing Data into a Process
      • Importing Data from Text Files
      • Importing Data from Json Files
      • Importing Data from Microsoft Excel
      • Importing Data from a Database
      • Importing Data from an XML File
      • Using Remote Connections
      • Conditionally Importing Data from Databases
      • Creating Datasets from Scratch
    • Exploring and Structuring Data
      • Reshaping, Transforming and Cleaning Datasets
        • Cleaning Datasets
        • Discretizing Data
        • Identifying and Managing Outliers
        • Reshaping Datasets to Wide Format
        • Reshaping Datasets to Long Format
        • Transposing Data
        • Performing Moving Windows Statistics on Data
      • Splitting Data into Training and Test Sets
        • Splitting Data with the Data Manager
        • Splitting Data with the Split Data Task
      • Exporting Data from a Process
        • Exporting Data to a Database
        • Exporting Data to an Excel File
        • Exporting Data to a Text File
        • Exporting Data to an XML File
        • Exporting Data from the Data Manager
        • Exporting Data to a Json File
      • Plotting Data in the Data Manager
        • Plotting Area Plots
        • Plotting Lorenz Curves
        • Plotting Q-Q Plots
        • Plotting P-P Plots
        • Merging Plots
        • Using Plots to Create What-If Scenarios
        • Plotting Stacked Bar Plots
        • Plotting Pie Charts
        • Plotting Curves
        • Plotting Scatters
        • Plotting ROC Curves
        • Viewing Multiple Plots
        • Plotting Heat Maps
        • Plotting Grouped Bar Plots
        • Plotting Box Plots
        • Customizing Plots
      • Data Manager Layout
      • Merging Datasets
        • Concatenating Datasets
        • Joining Datasets
      • Computing Statistics in the Data Manager
        • Computing Statistics - ROC curve
        • Computing Statistics - Values, Frequencies and Quantiles
        • Computing Statistics - Single Statistics
        • Computing Statistics - Cross Tabulation Statistics
        • Computing Statistics - Correlation and Covariance
        • Computing Statistics - Test for Independent Samples
        • Computing Statistics - Test for Paired Samples
      • Computing Formulas in the Data Manager
        • Common Operators in Data Manager Formulas
        • Examples of Formulas in Data Manager
        • Rulex Language Functions
        • Combining Formulas and Queries in Data Manager
      • Querying Data in the Data Manager
        • Filtering Data in the Query Manager
        • Grouping Data in the Query Manager
        • Applying Operations on Data in the Query Manager
        • Sorting Data in the Query Manager
      • Tracking Operations in the Data Manager
      • Managing Attributes in Data Manager
        • Managing Attribute Values
        • Managing Attribute Properties
      • Overview of Data Exploration in the Data Manager
      • Using External Scripts in Rulex
        • Applying R Scripts in Rulex Processes
        • Applying Python Scripts in Rulex Processes
        • Importing Data from R Scripts
        • Importing Data from Python Scripts
      • Identifying the Principal Components in Datasets with PCA
    • Solving your Problem
      • Solving Supervised Learning Problems
        • Using SVM to Solve Classification Problems
        • Using Decision Tree to Solve Classification Problems
        • Using Auto Regressive to Solve Regression Problems
        • Using K-Nearest Neighbor to Solve Regression Problems
        • Using K-Nearest Neighbor to Solve Classification Problems
        • Using Neural Networks to Solve Classification Problems
        • Using Neural Networks to Solve Regression Problems
        • Using Regression Tree to Solve Regression Problems
        • Using Regression SVM to Solve Regression Problems
        • Using Linear to Solve Regression Problems
        • Using LLM to Solve One-Class Problems
        • Using LLM to Solve Classification Problems
        • Using LLM to Solve Regression Problems
        • Using Logistic to Solve Classification Problems
      • Solving Unsupervised Learning Problems
        • Solving Association Problems in Rulex
          • Using Anomaly Detection to Solve Association Problems
          • Using Hierarchical Basket Analysis to Solve Association Problems
          • Using Frequent Itemsets Mining to Solve Association Problems
          • Using Sequence Analysis to Solve Association Problems
          • Using Assortment Optimizer to Solve Association Problems
          • Using Similar Items Detector to Solve Association Problems
        • Solving Clustering Problems
          • Using Label Clustering to Cluster Data
          • Using Projection Clustering to Cluster Data
          • Using Standard Clustering to Cluster Data
      • Solving Optimization Problems
        • Using Network Optimizer to Solve Optimization Problems
        • Using Mixed Integer Linear Programming to Solve Optimization Problems
    • Working with the Results
      • Converting Structures
        • Converting Rulesets to Datasets
        • Converting Datasets to Structures
        • Converting Models to Datasets
        • Converting Structures to Datasets
        • Converting Datasets to Models
        • Converting Datasets to Rulesets
      • Analyzing and Optimizing Results
        • Analyzing Rules in the Rule Manager
        • Adding Rules Manually in the Rule Manager
        • Analyzing Frequent Itemsets and Sequences
        • Optimizing Association and Replacement Rules
        • Ranking Rule Features and Values
        • Analyzing Model Performance in the Confusion Matrix
        • Analyzing Classification LLM Rules in the Rule Viewer
        • Merging Rules
        • Applying Models to Data
        • Finding and Replacing Values in Datasets
        • Optimizing Rulesets
    • Importing and Exporting Processes
      • Importing Structures with Import from Task
      • Exporting Rulex Process
        • Exporting Processes by Command
        • Exporting Processes by Task
      • Selecting Parts of a Process with Select Flow
      • Importing Rulex Processes
    • Compacting Tasks into Modules
      • Extracting Modules
      • Configuring Modules without Data Sources
      • Creating Modules without Data Sources
      • Creating Modules with Data Sources
      • Configuring Modules with Data Sources
    • Refining and Improving Processes
      • Using workflow variables in Rulex
        • Creating Process Variables
        • Configuring Runtime Variables
        • Connecting to Vault Variables
      • Managing Parametric Options
      • Setting Alerts for Tasks
      • Documenting Processes
      • Prioritizing Tasks
      • Creating and Using Macros
      • Improving the Appearance of Processes
    • Executing Rulex Processes
      • Scheduling Activities
      • Modifying Process Execution Parameters
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Working with the Results

When you have finished analyzing your data to solve your specific problem, you may want to also analyze and optimize the results.

Rulex offers specific tasks according to the problem you were solving:

  • Converting Structures
    • Converting Rulesets to Datasets
    • Converting Datasets to Structures
    • Converting Models to Datasets
    • Converting Structures to Datasets
    • Converting Datasets to Models
    • Converting Datasets to Rulesets
  • Analyzing and Optimizing Results
    • Analyzing Rules in the Rule Manager
    • Adding Rules Manually in the Rule Manager
    • Analyzing Frequent Itemsets and Sequences
    • Optimizing Association and Replacement Rules
    • Ranking Rule Features and Values
    • Analyzing Model Performance in the Confusion Matrix
    • Analyzing Classification LLM Rules in the Rule Viewer
    • Merging Rules
    • Applying Models to Data
    • Finding and Replacing Values in Datasets
    • Optimizing Rulesets

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