2022.2

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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
Clustering

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2022.2

What's New
Discover new features and enhancements.
Get Started
New to Activate? Learn the basics here.
Tutorials
Start using Activate with our interactive tutorials.
User Guide
Learn about the features and functionality available in Activate.
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
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  - Machine Learning Commands
    - Apriori
    - Association Rules
    - Classification
    - Clustering
      - agglomerativefit
        Agglomerative clustering. It is an unsupervised learning algorithm. It recursively merges the pair of clusters that minimally increase a given linkage distance.
      - agglomerativepredict
        Predicts target values for the test data points using parameters computed by agglomerativeFit function. In agglomerative clustering, again fitting is done with the given test data points and the options set during training. So instead can use the labels of the output of fit method.
      - kmeansfit
        K-Means clustering. It's an unsupervised learning algorithm.
      - kmeanspredict
        Predicts target values for the test data points using parameters computed by kmeansFit function.
    - Decomposition
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    - Evaluation Metrics
    - K-Mode Clustering
    - Model Selection
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  - Optimization Commands
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  - OML to Python Bridge Commands
  - Python to OML Bridge Commands
- Block Reference Guide
  Describes all of the blocks in the installed Activate library.
- Function Guide for Activate
  Provides information about developing and simulating models through the Activate Application Program Interface.
- Glossary
  Key terms associated with the software.
Frequently Asked Questions
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View All Altair Simulation Help

2022.2

Home
Reference Guides
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
Clustering

What's New
Get Started
Tutorials
User Guide
Reference Guides
Frequently Asked Questions

Clustering

The clustering, which is the unsupervised grouping task of unlabelled data, can be carried out using the following methods:

Agglomerative
KMeans

agglomerativefit
Agglomerative clustering. It is an unsupervised learning algorithm. It recursively merges the pair of clusters that minimally increase a given linkage distance.
agglomerativepredict
Predicts target values for the test data points using parameters computed by agglomerativeFit function. In agglomerative clustering, again fitting is done with the given test data points and the options set during training. So instead can use the labels of the output of fit method.
kmeansfit
K-Means clustering. It's an unsupervised learning algorithm.
kmeanspredict
Predicts target values for the test data points using parameters computed by kmeansFit function.

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