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Explore the Extended Definitions, OML Guides, Block Library, API Guide and Glossary.
Reference Guide for OpenMatrix Language Functions
The Reference Guide contains documentation for all functions supported in the OpenMatrix language.
Machine Learning Commands
K-Mode Clustering

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Reference Guides
Explore the Extended Definitions, OML Guides, Block Library, API Guide and Glossary.
- Extended Definitions for Advanced Users
  PDF file with in-depth information on key topics in the User's Guide.
- Scripting Guide for OpenMatrix Language
  OpenMatrix is a mathematical scripting language.
- Reference Guide for OpenMatrix Language Functions
  The Reference Guide contains documentation for all functions supported in the OpenMatrix language.
  - ABF Commands
  - Altair Integration Commands
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  - hstpyfit Commands
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  - JSON Commands
  - Library Manager Commands
  - Linear Algebra Commands
  - Logical Commands
  - Machine Learning Commands
    - Apriori
    - Association Rules
    - Classification
    - Clustering
    - Decomposition
    - Distance
    - Evaluation Metrics
    - K-Mode Clustering
      - kmodes_fit
        Returns a structure with all required k-modes fitted model parameters. These parameters are used for prediction.
      - kmodes_predict
        Returns a list of all predicted clusters for the dataset and model parameters provided.
    - Model Selection
    - Preprocessing
    - Regression
  - Optimization Commands
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  - Polynomial Math Commands
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  - String Operations Commands
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  - Table Commands
  - Time Commands
  - Trigonometry Commands
  - OML to Python Bridge Commands
  - Python to OML Bridge Commands
- Block Reference Guide
  Describes all of the blocks in the installed Twin Activate library.
- Function Guide for Twin Activate
  Provides information about developing and simulating models through the Twin Activate Application Program Interface.
- Glossary
  Key terms associated with the software.
Frequently Asked Questions
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K-Mode Clustering

Attention: Available only with Twin Activate commercial edition.

Unsupervised machine learning to cluster categorical variables.